Systems and methods for determining optimum patient-specific antibody dose for tumor targeting

ABSTRACT

The present disclosure describes a non-linear compartmental model using PET-derived data to predict, on a patient-specific basis, the optimal therapeutic dose of cargo carrying antibody (e.g., huA33) such as radiolabeled antibody, the antigen occupancy, residency times in normal and malignant tissues, and the cancer-to-normal tissue (e.g., colorectal cancer-to-normal colon tissue) therapeutic index. In addition, the non-linear compartmental model can be readily applied to the development of strategies such as multi-step targeting (MST) designed to further improve the therapeutic indices of RIT.

CROSS REFERENCE TO RELATED APPLICATION

This application claims the benefit of U.S. Application Ser. No.62/165,699 filed on May 22, 2015, the disclosure of which is herebyincorporated by reference in its entirety.

GOVERNMENT FUNDING

This invention was made with government support under Grant. No. R21 CA153177-03 awarded by the National Cancer Institute (NCI) Center toReduce Cancer Health Disparities; Grant. No. P50 CA086438 awarded byNCI; Grant Nos. PO1 CA 33049, R25-CA096945, R24-CA83084, P30-CA08748,and P50-CA92629 awarded by the National Institutes of Health (NIH). Thegovernment has certain rights in this invention.

FIELD OF THE INVENTION

This invention relates generally to non-invasive positron emissiontomography (PET) imaging of targeted antibody radiotracer uptake andsubsequent metabolism. In particular embodiments, the invention relatesto a PET-based non-linear compartmental modeling for individualizingtumor targeting of antibodies.

BACKGROUND

Over the last decade, monoclonal antibodies (mAbs) as a class of drugshave achieved critical importance in oncology both clinically andcommercially. It is projected, for example, that in the near future fourof the five top oncology products in the world will be antibody-basedpharmaceuticals, such as rituximab, trastuzumab, bevacizumab, cetuximab,other mAbs (e.g., immune effectors), and targeting vectors fortherapeutic drugs and/or radioisotopes (e.g., ado-trastuzumabemtansine). Moreover, it is predicted that global sales will increasefrom $48 billion in 2008 to $75-80 billion in the near future.Nonetheless, despite the clinical and commercial importance ofantibodies to oncology, factors which impact antibody in vivobiodistribution and their biologic effectiveness remain poorlyunderstood. As a result, optimized dose and dosing scheduling continueto be determined empirically by laborious, time-consuming, and costlydose-escalation studies.

The molecular specificity of monoclonal antibodies directed againsttumor antigens has proven effective for targeted therapy for humancancers. For example, clinical studies with murine anti-A33 mAb havedemonstrated its safety, tumor-targeting ability, and specificity, eventhough development of human anti-mouse antibody (HAMA) was common andresulted in accelerated clearance of subsequent administrations of themAb. Initial trials with huA33 alone or combined with chemotherapy haveshown favorable tolerability, some evidence of therapeutic response, andless immunogenicity than murine anti-A33 mAb.

Prior work by the inventors determined the uptake and kinetics of an¹²⁴I-labeled humanized anti-A33 mAb, huA33, in tumor and normal-tissueof colorectal cancer patients using positron emission tomography (PET)with tissue sampling at surgery (O'Donoghue J A, Smith-Jones P M, Humm JL, et al.: 124I-huA33 antibody uptake is driven by A33 antigenconcentration in tissues from colorectal cancer patients imaged byimmuno-PET. J Nucl Med. 52:1878-85, 2011; Carrasquillo J A,Pandit-Taskar N, O'Donoghue J A, et al.: (124)I-huA33 antibody PET ofcolorectal cancer. J Nucl Med. 52:1173-80, 2011, each of which is herebyincorporated by reference in its entirety).

It was determined that PET imaging can accurately and non-invasivelyquantitate the time-dependent antibody concentrations in vivo. Moreover,it was found that the “equilibrium” uptake of radiolabeled antibody(e.g., huA33) in antigen (e.g., A33)-expressing normal and tumor tissueswas driven primarily by the antigen concentration and such uptake wassaturable.

Current dosing models use linear models that are not patient-specific.However, because of the saturability of the antibody-antigen interactiondescribed above, non-linear analysis would be required forpatient-specific optimization of therapeutic antibody-targetingstrategies. For example, the concentration of A33 in targeted tumors,the total mass of the A33-expressing tumor, and the total number of A33binding sites can vary among patients. Moreover, the anti-A33 mAb-to-A33exchange rate in vivo, the tumor concentration, and tumor-to-normaltissue ratios of the antibody also varies patient-to-patient. The latterparameters change as the dose of antibody (in moles) is varied. As aresult, even if adjusted for body mass, body surface area, etc., thereis no single optimum antibody dose which is translatable from patient topatient.

Therefore, there is a need for an approach that optimizesradioimmunotherapy (RIT) for individual patients. Individualizing tumortherapy requires administration of a pre-therapy tracer of the mAb tothe patient, an imaging-based measurement of its time-activity data inblood, tumor, and antigen-expressing normal tissue(s), fitting of thesedata to the non-linear model, and model-based determination of theoptimum antibody dose (in moles) for that patient.

SUMMARY OF INVENTION

Disclosed herein is a non-linear compartmental model using PET-deriveddata to determine the best-fit parameters and model-derived quantitiesfor optimizing biodistribution of intravenously injected anti-tumorantibodies for patient-specific targeting in colorectal cancer. Incertain embodiments, the disclosed model is based on the A33 antibody toimprove radioimmunotherapy methodologies, which are subject to empiricaldetermination of the optimized dose and dosing scheduling by laborious,time-consuming and costly dose-escalation studies. In certainembodiments, the disclosed model is based on the hu11B6 antibody toimprove radioimmunotherapy methodologies, which are subject to empiricaldetermination of the optimized dose and dosing scheduling by laborious,time-consuming and costly dose-escalation studies. In certainembodiments, the disclosed model is based on the J591 antibody toimprove radioimmunotherapy methodologies, which are subject to empiricaldetermination of the optimized dose and dosing scheduling by laborious,time-consuming and costly dose-escalation studies. In certainembodiments, the disclosed model is based on any antibody to improveradioimmunotherapy methodologies, which are subject to empiricaldetermination of the optimized dose and dosing scheduling by laborious,time-consuming and costly dose-escalation studies.

The disclosed compartmental-modeling approach can be used to predict, ona patient-specific basis, the optimal therapeutic dose of a radiolabeledantibody, including but not limited to huA33, the antigen occupancy,residency times in normal and malignant tissues, and thecancer-to-normal tissue therapeutic index (e.g., colorectalcancer-to-normal colon therapeutic index). In addition, non-linearcompartmental modeling can be readily applied to the development ofstrategies such as multi-step targeting (MST) designed to furtherimprove the therapeutic indices of RIT. In certain embodiments, thedisclosed non-linear compartmental modeling approach is used to predicttumor exposure of chemotherapies, toxins, and other cargos (e.g., immuneeffectors, fluorescent probes, porphyrins, radiographic contrast agents(e.g., metals, MRI agents)) that are transported and bound to anantibody.

In one aspect, the invention is directed to a method for determining apatient-specific optimum mAb dose, the method comprising: administeringto a subject a radiolabeled monoclonal antibody (mAb) as pre-therapytracer of the mAb; imaging the subject to obtain a plurality of sets ofimages acquired over a period of time, wherein each set of images isacquired at a particular time following administration of theradiolabeled monoclonal antibody, and wherein each set of imagesvisualizes a blood pool, tumor, and antigen-positive normal tissue;determining, by a processor of a computing device, from the plurality ofsets of images, measurements of time-activity of the pre-therapy tracerof the mAb in blood, tumor, and antigen-expressing normal tissue for thesubject; identifying, by the processor, one or more parameters of anonlinear compartmental model of the kinetics/biodistribution of thepre-therapy tracer in the subject from the determined measurements oftime-activity for the subject; and determining, by the processor, apatient-specific optimal therapeutic antibody dose (or multiple doses)for the subject using the nonlinear compartmental model with theidentified one or more parameters.

In certain embodiments, the monoclonal antibody is selected from thegroup consisting of A33 antibody, hu11B6 antibody, J591 antibody,trastuzumab, MSTP2109A, Df-IAB2M, cmAb-U36, ibritumomab tiuxetan,bevacizumab, rituximab, any antibody targeting human epidermal growthfactor receptor 3 (e.g., GSK2849330), and any antibody targeting humancarcinoembryonic antigen (e.g., AMG11).

In certain embodiments, the radiolabel is selected from the groupconsisting of ⁹⁹Tc, ¹¹¹In, ⁶⁴Cu, ⁶⁷Ga, ¹⁸⁶Re, ¹⁸⁸Re, ¹⁵³Sm, ¹⁷⁷Lu, ⁶⁷Cu,¹²³I, ¹²⁴I, ¹²⁵I, ¹¹C, ¹3N, ¹⁵O, ¹⁸F, ¹⁸⁶Re, ¹⁸⁸Re, ¹⁵³Sm, ¹⁶⁶Ho, ¹⁷⁷Lu,¹⁴⁹Pm, ⁹⁰Y, ²¹³Bi, ¹⁰³Pd, ¹⁰⁹Pd, ¹⁵⁹Gd, ¹⁴⁰La, ¹⁹⁸Au, ¹⁹⁹Au, ¹⁶⁹Yb,¹⁷⁵Yb, ¹⁶⁵Dy, ¹⁶⁶Dy, ⁶⁷Cu, ¹⁰⁵Rh, ¹¹¹Ag, ⁸⁹Zr, ²²⁵Ac, ¹³¹I, ²¹²B, and¹⁹²Ir. In certain embodiments, the radiolabel comprises ⁸⁹Zr. In certainembodiments, the radiolabel comprises ²²⁵Ac. In certain embodiments, theradiolabel comprises ¹²⁴I.

In certain embodiments, the method further comprises acquiring theplurality of sets of images via positron emission tomography (PET).

In certain embodiments, a first set of the images is acquired severalhours after antibody administration and at least a second set of theimages is acquired up to 2 weeks after antibody administration.

In certain embodiments, the first set of images is acquired at least 2hours, at least 3 hours, at least 4 hours, or at least 5 hours afterantibody administration.

In certain embodiments, the antigen-positive normal tissue comprisescolon tissue (e.g., wherein the monoclonal antibody is A33 antibody). Incertain embodiments, the antigen-positive normal tissue comprises breasttissue (e.g., wherein the monoclonal antibody is hu11B6 antibody). Incertain embodiments, the antigen-positive normal tissues compriseprostate tissue (e.g., wherein the monoclonal antibody is J591antibody).

In certain embodiments, the therapeutic antibody is the same monoclonalantibody as in the pre-therapy tracer. In certain embodiments, thetherapeutic antibody is different than the monoclonal antibody in thepre-therapy tracer.

In certain embodiments, the measurements of time-activity of thepre-therapy tracer comprise one or more members selected from the groupconsisting of a mean activity concentration of the radiolabeled antibodyin the blood, a mean activity concentration of the radiolabeled antibodyin the tumor, and a mean activity concentration of the radiolabeledantibody in the antigen-expressing normal tissue.

In certain embodiments, the antigen-expressing normal tissue comprises amember selected from the group consisting of bowel tissue, breasttissue, prostate tissue, kidney, liver, salivary gland, and lung.

In certain embodiments, the method further comprises imaging the subjectvia computed tomography (CT) or magnetic resonance imaging (MRI) todetermine a tumor mass.

In certain embodiments, a mean activity concentration of theradiolabeled antibody in the tumor is determined, by the processor, froma total activity concentration by multiplying the tumor mass by thetotal activity concentration and an elapsed time after the administeringstep.

In certain embodiments, the radiolabeled mAb comprises a radiolabel witha half-life compatible with imaging-based activity concentrationmeasurements in tumor and other tissues.

In certain embodiments, the half-life is consistent with the turnoverrate of intestinal mucosa in the case of A33 mAb.

In certain embodiments, the radiolabeled mAb is administered over aperiod of time from 5 minutes to 20 minutes.

In certain embodiments, the administering step comprises administeringtwo or more doses of the pre-therapy tracer at different times, andwherein the method further comprises determining, by the processor, oneor more optimum times of administration of the pre-therapy tracer(non-radiolabeled mAb).

In certain embodiments, the pre-therapy tracer comprises a bi-specifictetravalent antibody/-[metal-DOTA] antibody construct (nALDO, orhuA33-C825) which reacts with an antigen and with radiometal-labeledDOTA-Bn (DOTA-Bn:(2-(4-aminobenzyl)-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetrayl)tetraacetic acid).

In certain embodiments, the antibody is a member selected from the groupconsisting of A33 antibody, hu11B6 antibody, and J591 antibody and theantigen is a member selected from the group consisting of A33, hu11B6,and J591.

In certain embodiments, the nonlinear compartmental model comprises oneor more of the following assumptions: (i) no free radioiodine orradioiodinated metabolites of the radiolabeled mAb are produced in vivo;(ii) there is no significant amount of free (non-bound) radiolabeled mAbin normal tissue and in tumor tissue; (iii) there is no significantdissociation of the bound radiolabeled mAb from the antigen; and (iv)the mAb-antigen association rate constant (ka) is the same for antigenin normal tissue and in tumor.

In certain embodiments, the radiolabeled mAb is a member selected fromthe group consisting of ¹²⁴I-labeled anti-A33, ⁸⁹Zr-labeled anti-hu11B6,⁸⁹Zr-labeled anti-J591, ⁸⁹Zr-trastuzumab, ⁸⁹Zr-MSTP2109A, ⁸⁹Zr-Df-IAB2M,⁸⁹Zr-cmAb-U36, ⁸⁹Zr-ibritumomab tiuxetan, ⁸⁹Zr-bevacizumab, and⁸⁹Zr-rituximab.

In certain embodiments, the antigen is a member selected from the groupconsisting of A33, hu11B6, J591, GPA33, kallikrein hk2, and prostatespecific membrane (PSMA).

In certain embodiments, the tissue (e.g., any diseased tissue) is amember selected from the group consisting of bowel tissue, breasttissue, prostate tissue, kidney, liver, salivary gland, and lung.

In certain embodiments, the method comprises determining, by theprocessor, a quantification of internalization and turnover of anantibody-cargo via the nonlinear compartmental model.

In certain embodiments, the antibody-cargo comprises a member selectedfrom the group consisting of a radiolabel, anti-cancer drug, immuneeffectors, fluorescent probes, porphyrins, radiographic contrast agents(e.g., metals, MRI agents), and cytotoxin.

In certain embodiments, the anti-cancer drug comprises a member selectedfrom the group consisting of calicheamicin, doxirubicin,dolstatin/auristatin, maytansine, emtansine, ravtansine, alpha amanitin,pyrolobenzodiazapine, tubulysins, rituximab, trastuzumab, bevacizumab,anti-huJ591, MSTP2109A, Df-IAB2M, cmAb-U36, ibritumomab tiuxetan,bevacizumab, rituximab, any antibody targeting human epidermal growthfactor receptor 3 (e.g., GSK2849330), and any antibody targeting humancarcinoembryonic antigen (e.g., AMG11), dasatinib and other pathwayinhibitors, bevatuzimab, cetuximab, and at least one drug depicted inTable 2.

In certain embodiments, the radiolabel comprises a member selected fromthe group consisting of ²²⁵Ac, ¹⁷⁷Lu, ²¹¹At, ¹³¹I, ¹²⁴I, ⁹⁰Y, ⁶⁴Cu,⁶⁸Ga, ⁶⁶Ga, and ⁶⁷Cu.

In certain embodiments, the nonlinear compartmental model comprises arepresentation of an interaction of the pre-therapy tracer of the mAbwith tissue.

In certain embodiments, the interaction comprises a member selected fromthe group consisting of targeting antibody affinity, antigen density,and internalization rates of antibody-antigen complex.

In certain embodiments, the nonlinear compartmental model is as depictedin FIG. 2.

In certain embodiments, the administering step comprises administering amonoclonal antibody with a first cargo and a second cargo, the methodfurther comprising estimating, by the processor, a residence time of thefirst and second antibody cargos associated with the same (type of) mAb.

In certain embodiments, the method further comprises administering thetherapeutic antibody dose (or multiple doses) to the subject.

In certain embodiments, the administration is via injection.

In certain embodiments, the one or more parameters comprises a targetingindex.

In certain embodiments, the targeting index comprises a tumor-to-plasmaratio of integrals of tumor and plasma activity concentrations.

In certain embodiments, the one or more parameters comprises atherapeutic index.

In certain embodiments, the therapeutic index comprises a PETimage-derived tumor-to-normal tissue ratio (e.g., area-under-the-curve(AUC) concentration (% ID/g·h) ratio).

In certain embodiments, the tumor-to-normal tissue comprises a memberselected from the group consisting of tumor-to-bowel mucosa,tumor-to-breast, tumor-to-prostate, tumor-to-lung, tumor-to-kidney,tumor-to-liver, tumor-to-salivary gland, tumor-to-bone marrow,tumor-to-liver, tumor-to-lung, tumor-to-kidney, tumor-to-bowel,tumor-to-bladder, tumor-to-gonads, and tumor-to-brain.

In another aspect, the invention is directed to a method of determininga patient-specific optimum mAb dose, the method comprising: receiving,by a processor of a computing device, a plurality of sets of imagesacquired over a period of time, wherein each set of images is acquiredat a particular time following administration of the radiolabeledmonoclonal antibody, and wherein each set of images visualizes a bloodpool, tumor, and antigen-positive normal tissue; determining, by theprocessor, from the plurality of sets of images, measurements oftime-activity of the pre-therapy tracer of the mAb in blood, tumor, andantigen-expressing normal tissue for the subject; identifying, by theprocessor, one or more parameters of a nonlinear compartmental model ofthe kinetics/biodistribution of the pre-therapy tracer in the subjectfrom the determined measurements of time-activity for the subject; anddetermining, by the processor, an optimum therapeutic antibody dose (ormultiple doses) for the subject using the nonlinear compartmental modelwith the identified one or more parameters.

In certain embodiments, the method further comprises acquiring theplurality of sets of images via positron emission tomography (PET).

In certain embodiments, a first set of the images is acquired severalhours after post-antibody administration and at least a second set ofthe images is acquired up to 2 weeks after antibody administration.

In certain embodiments, the first set of images is acquired at least 2hours, at least 3 hours, at least 4 hours, or at least 5 hours afterpost-antibody administration.

In certain embodiments, the antigen-positive normal tissue comprisescolon tissue (e.g., wherein the radiolabeled monoclonal antibodycomprises A33 antibody). In certain embodiments, the antigen-positivenormal tissue comprises breast tissue (e.g., wherein the radiolabeledmonoclonal antibody comprises hu11B6 antibody). In certain embodiments,the antigen-positive normal tissue comprises prostate tissue (e.g.,wherein the radiolabeled monoclonal antibody comprises J591 antibody).

In certain embodiments, the therapeutic antibody is the same monoclonalantibody as in the pre-therapy tracer. In certain embodiments, thetherapeutic antibody is different than the monoclonal antibody in thepre-therapy tracer.

In certain embodiments, the one or more parameters comprise a targetingindex.

In certain embodiments, the targeting index comprises a tumor-to-plasmaratio of integrals of tumor and plasma activity concentrations.

In certain embodiments, the one or more parameters comprises atherapeutic index.

Elements of embodiments involving one aspect of the invention (e.g.,methods) can be applied in embodiments involving other aspects of theinvention (e.g., systems), and vice versa.

Definitions

In order for the present disclosure to be more readily understood,certain terms are first defined below. Additional definitions for thefollowing terms and other terms are set forth throughout thespecification.

In this application, the use of “or” means “and/or” unless statedotherwise. As used in this application, the term “comprise” andvariations of the term, such as “comprising” and “comprises,” are notintended to exclude other additives, components, integers or steps. Asused in this application, the terms “about” and “approximately” are usedas equivalents. Any numerals used in this application with or withoutabout/approximately are meant to cover any normal fluctuationsappreciated by one of ordinary skill in the relevant art. In certainembodiments, the term “approximately” or “about” refers to a range ofvalues that fall within 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%,12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or less in eitherdirection (greater than or less than) of the stated reference valueunless otherwise stated or otherwise evident from the context (exceptwhere such number would exceed 100% of a possible value).

“Administration”: The term “administration” refers to introducing asubstance into a subject. In general, any route of administration may beutilized including, for example, parenteral (e.g., intravenous), oral,topical, subcutaneous, peritoneal, intraarterial, inhalation, vaginal,rectal, nasal, introduction into the cerebrospinal fluid, orinstillation into body compartments. In some embodiments, administrationis oral. Additionally or alternatively, in some embodiments,administration is parenteral. In some embodiments, administration isintravenous.

“Approximately”: As used herein, the term “approximately” or “about,” asapplied to one or more values of interest, refers to a value that issimilar to a stated reference value. In certain embodiments, the term“approximately” or “about” refers to a range of values that fall within25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%,6%, 5%, 4%, 3%, 2%, 1%, or less in either direction (greater than orless than) of the stated reference value unless otherwise stated orotherwise evident from the context (except where such number wouldexceed 100% of a possible value).

“Biocompatible”: The term “biocompatible”, as used herein is intended todescribe materials that do not elicit a substantial detrimental responsein vivo. In certain embodiments, the materials are “biocompatible” ifthey are not toxic to cells. In certain embodiments, materials are“biocompatible” if their addition to cells in vitro results in less thanor equal to 20% cell death, and/or their administration in vivo does notinduce inflammation or other such adverse effects. In certainembodiments, materials are biodegradable.

“Biodegradable”: As used herein, “biodegradable” materials are thosethat, when introduced into cells, are broken down by cellular machinery(e.g., enzymatic degradation) or by hydrolysis into components thatcells can either reuse or dispose of without significant toxic effectson the cells. In certain embodiments, components generated by breakdownof a biodegradable material do not induce inflammation and/or otheradverse effects in vivo. In some embodiments, biodegradable materialsare enzymatically broken down. Alternatively or additionally, in someembodiments, biodegradable materials are broken down by hydrolysis. Insome embodiments, biodegradable polymeric materials break down intotheir component polymers. In some embodiments, breakdown ofbiodegradable materials (including, for example, biodegradable polymericmaterials) includes hydrolysis of ester bonds. In some embodiments,breakdown of materials (including, for example, biodegradable polymericmaterials) includes cleavage of urethane linkages.

“Carrier”: As used herein, “carrier” refers to a diluent, adjuvant,excipient, or vehicle with which the compound is administered. Suchpharmaceutical carriers can be sterile liquids, such as water and oils,including those of petroleum, animal, vegetable or synthetic origin,such as peanut oil, soybean oil, mineral oil, sesame oil and the like.Water or aqueous solution saline solutions and aqueous dextrose andglycerol solutions are preferably employed as carriers, particularly forinjectable solutions. Suitable pharmaceutical carriers are described in“Remington's Pharmaceutical Sciences” by E. W. Martin.

“Excipient”: As used herein, the term “excipient” refers to anon-therapeutic agent that may be included in a pharmaceuticalcomposition, for example to provide or contribute to a desiredconsistency or stabilizing effect. Suitable pharmaceutical excipientsinclude, for example, starch, glucose, lactose, sucrose, gelatin, malt,rice, flour, chalk, silica gel, sodium stearate, glycerol monostearate,talc, sodium chloride, dried skim milk, glycerol, propylene, glycol,water, ethanol and the like.

“Image”: As used herein, an “image” includes any visual representation,such as a photo, a video frame, streaming video, as well as anyelectronic, digital or mathematical analogue of a photo, video frame, orstreaming video. Any apparatus described herein, in certain embodiments,includes a display for displaying an image or any other result producedby the processor. Any method described herein, in certain embodiments,includes a step of displaying an image or any other result produced viathe method. An image may be rendered as a dataset in two or threedimensions and/or may be displayed as one or more two-dimensionalrepresentations, or as a three-dimensional representation.

“Radiolabel”: As used herein, “radiolabel” refers to a moiety comprisinga radioactive isotope of at least one element. Exemplary suitableradiolabels include but are not limited to those described herein. Insome embodiments, a radiolabel is one used in positron emissiontomography (PET). In some embodiments, a radiolabel is one used insingle-photon emission computed tomography (SPECT). In some embodiments,radioisotopes comprise ^(99m)Tc, ¹¹¹In, ⁶⁴Cu, ⁶⁷Ga, ¹⁸⁶Re, ¹⁸⁸Re, ¹⁵³Sm,¹⁷⁷Lu, ⁶⁷Cu, ¹²³I, ¹²⁴I, ¹²⁵I, ¹¹C, ¹³N, ¹⁵O, ¹⁸F, ¹⁸⁶Re, ¹⁸⁸Re, ¹⁵³Sm,¹⁶⁶Ho, ¹⁷⁷Lu, ¹⁴⁹Pm, ⁹⁰Y, ²¹³Bi, ¹⁰³Pd, ¹⁰⁹Pd, ¹⁵⁹Gd, ¹⁴⁰La, ¹⁹⁸Au,¹⁹⁹Au, ¹⁶⁹Yb, ¹⁷⁵Yb, ¹⁶⁵Dy, ¹⁶⁶Dy, ⁶⁷Cu, ¹⁰⁵Rh, ¹¹¹Ag, ⁸⁹Zr, ²²⁵Ac,²¹¹At, ¹³¹I, ¹³¹I, ²¹²B, and ¹⁹²Ir.

“Substantially: As used herein, the term “substantially”, andgrammatical equivalents, refers to the qualitative condition ofexhibiting total or near-total extent or degree of a characteristic orproperty of interest. One of ordinary skill in the art will understandthat biological and chemical phenomena rarely, if ever, go to completionand/or proceed to completeness or achieve or avoid an absolute result.

“Subject”: As used herein, the term “subject” includes humans andmammals (e.g., mice, rats, pigs, cats, dogs, and horses). In manyembodiments, subjects are mammals, particularly primates, especiallyhumans. In some embodiments, subjects are livestock such as cattle,sheep, goats, cows, swine, and the like; poultry such as chickens,ducks, geese, turkeys, and the like; and domesticated animalsparticularly pets such as dogs and cats. In some embodiments (e.g.,particularly in research contexts) subject mammals will be, for example,rodents (e.g., mice, rats, hamsters), rabbits, primates, or swine suchas inbred pigs and the like.

“Therapeutic agent”: As used herein, the phrase “therapeutic agent”refers to any agent that has a therapeutic effect and/or elicits adesired biological and/or pharmacological effect, when administered to asubject.

“Treatment”: As used herein, the term “treatment” (also “treat” or“treating”) refers to any administration of a substance that partiallyor completely alleviates, ameliorates, relives, inhibits, delays onsetof, reduces severity of, and/or reduces incidence of one or moresymptoms, features, and/or causes of a particular disease, disorder,and/or condition. Such treatment may be of a subject who does notexhibit signs of the relevant disease, disorder and/or condition and/orof a subject who exhibits only early signs of the disease, disorder,and/or condition. Alternatively or additionally, such treatment may beof a subject who exhibits one or more established signs of the relevantdisease, disorder and/or condition. In some embodiments, treatment maybe of a subject who has been diagnosed as suffering from the relevantdisease, disorder, and/or condition. In some embodiments, treatment maybe of a subject known to have one or more susceptibility factors thatare statistically correlated with increased risk of development of therelevant disease, disorder, and/or condition.

Drawings are presented herein for illustration purposes, not forlimitation.

BRIEF DESCRIPTION OF DRAWINGS

The foregoing and other objects, aspects, features, and advantages ofthe present disclosure will become more apparent and better understoodby referring to the following description taken in conduction with theaccompanying drawings, in which:

FIG. 1 shows coronal PET images of a patient with colorectal cancermetastatic to liver at 2 days and 7 days after injection of ¹²⁴I-huA33mAb. The images shown are maximum-intensity projections (MIPs). The2-day image shows two high-uptake foci in the liver (single arrow) andsplenic flexure (double arrow) corresponding to known metastatic lesionsand some uptake in bowel. At 7 days, there is persistent uptake in theliver metastases and prominent uptake in normal bowel (triple arrow).For this patient, the model-derived A33 concentrations in tumor andnormal bowel were 500 nM and 20 nM, respectively, and the maximum A33occupancies 10% and 1%, respectively. These occupancy values are at thelow end of the range for the patient cohort as shown in Table 1.

FIG. 2 shows a non-linear compartmental model of systemicallyadministered anti-A33 mAb in patient with A33-expressing tumors. Thesquare brackets indicate concentrations (in M), the indices 1, 2, and 3correspond to plasma, normal bowel, and tumor, respectively (asindicated), k(i,j) is the fractional exchange rate of anti-A33 mAb tocompartment i from compartment j (in/h), Q_(i) is the amount of anti-A33mAb in compartment i (in mole), Vi is the volume of compartment i (in 1)(equivalent to the mass of tissue i in kg), k(0,i) is the rate ofelimination of ¹²⁴I-mAb from compartment i (in/h), Flux (i,j) is theabsolute exchange rate of anti-A33 mAb to compartment i from compartmentj (in mole/h), and ka is the association rate constant for the bindingof the anti-A33 mAb to A33 (in/M/h). Evaluation of the values of thevarious “k” parameters in the model for specific patients is performedby computerized fitting of the model to the patient's measuredtime-activity data, that is, solving of the system of differentialequations in FIG. 2.

FIG. 3 shows time-activity data % ID/g versus time post-injection) forplasma, A33-expressing colorectal tumor, and normal colon followingintravenous injection of ¹²⁴I-A33 into a colorectal cancer patient. Thepoints represent the measured data and the curves the “best-fit”model-derived data obtained using the model in FIG. 2. For this case,the best-fit model-derived tumor and normal-colon A33 concentrationswere 2.0 nM and 4.5 nM, respectively, and the antibody-antigenassociation rate constant 6.5×10⁻⁹/M/hr.

FIG. 4 is a block diagram of an example network environment for use inthe methods and systems for analysis of spectrometry data, according toan illustrative embodiment.

FIG. 5 is a block diagram of an example computing device and an examplemobile computing device, for use in illustrative embodiments of theinvention.

FIG. 6 shows that the biodistributions (or the percent of theadministered activity per gram of tissue, % IA/g) at 244 hourspost-administration of ⁸⁹Zr-DFO-hu11B6 and ²²⁵Ac-DOTA-hu11B6 antibodytargeting breast cancer in mice bearing human BT474 breast tumorxenografts are shown, within experimental error, to be identical(lg-int: large intestine; sm-int: small intestine).

FIG. 7 show an embodiment of the non-linear compartmental model appliedto a quantitative comparison of absolute uptake and antibody turnover inprostate cancer (PCa) of the anti-prostate-specific membrane antigen(PSMA) antibody J591 labeled with ⁸⁹Zr (or ⁸⁹Zr-J591).

DETAILED DESCRIPTION

Throughout the description, where compositions are described as having,including, or comprising specific components, or where methods aredescribed as having, including, or comprising specific steps, it iscontemplated that, additionally, there are compositions of the presentinvention that consist essentially of, or consist of, the recitedcomponents, and that there are methods according to the presentinvention that consist essentially of, or consist of, the recitedprocessing steps.

It should be understood that the order of steps or order for performingcertain action is immaterial so long as the invention remains operable.Moreover, two or more steps or actions may be conducted simultaneously.

The mention herein of any publication, for example, in the Backgroundsection, is not an admission that the publication serves as prior artwith respect to any of the claims presented herein. The Backgroundsection is presented for purposes of clarity and is not meant as adescription of prior art with respect to any claim.

The molecular specificity of monoclonal antibodies (mAbs) directedagainst tumor antigens has proven effective for targeted therapy ofhuman cancers, as witnessed by a growing list of successfulantibody-based drug products.

Described herein is a non-linear compartmental model using positronemission tomography (PET)-derived data to determine the “best-fit”parameters and model-derived quantities for optimizing biodistributionof intravenously injected radiolabeled-labeled anti-tumor antibodies. Asan example of this paradigm, quantitative image and kinetic analyses ofanti-A33 humanized mAb (also known as “A33”) were performed incolorectal cancer patients (n=11). Serial whole-body PET scans of¹²⁴I-labeled A33 and blood samples were acquired, and the resultingtissue time-activity data for each patient were fit to a non-linearcompartmental model using the SAAM II (Simulation, Analysis, andModeling) computer code. Agreement was observed between fitted andmeasured parameters of tumor uptake, “off-target” uptake in bowelmucosa, blood clearance, tumor antigen levels, and percent antigenoccupancy.

This approach was applied to, but is not limited to, variousantibody-antigen systems in human tumors for which the masses ofantigen-expressing tumor and normal tissues were estimated. Moreover,this approach was used to measure antibody kinetics with PET. Thus, thisapproach can enable a patient-specific optimum mAb dose (in μmol, forexample) to be derived based on each patient's resulting “best-fit”non-linear model.

In certain embodiments, the methodology described is applicable tomulti-step targeting strategies wherein a non-radiolabeled mAb is firstadministered followed by a radiolabeled reagent which cross-reacts withthat mAb as well as a directly radiolabeled mAb.

In certain embodiments, the radiolabel is monitored and/or imaged (e.g.,via PET or single-photon emission computed tomography (SPECT)). Exampleradiolabels that can be used include beta emitters (e.g., ¹⁷⁷Luteium)and alpha emitters (e.g., ²²⁵Ac) In certain embodiments, one or more ofthe following radioisotopes are used: ^(99m)Tc, ⁶⁴Cu, ⁶⁷Ga, ¹⁸⁶Re,¹⁸⁸Re, ¹⁵³Sm, ¹⁷⁷Lu, ⁶⁷Cu, ¹²³I, ¹²⁴I, ¹²⁵I, ¹¹C, ¹3N, ¹⁵O, ¹⁸F, ¹⁸⁶Re,¹⁸⁸Re, ¹⁵³Sm, ¹⁶⁶Ho, ¹⁷⁷Lu, ¹⁴⁹Pm, ⁹⁰Y, ²¹³Bi, ¹⁰³Pd, ¹⁰⁹Pd, ¹⁵⁹Gd,¹⁴⁰La, ¹⁹⁸Au, ¹⁹⁹Au, ¹⁶⁹Yb, ¹⁷⁵Yb, ¹⁶⁵Dy, ¹⁶⁶Dy, ⁶⁷Cu, ¹⁰⁵Rh, ¹¹¹Ag,⁸⁹Zr, ²²⁵Ac, ¹³¹I, ²¹²B, and ¹⁹²Ir.

In certain embodiments, a chelator, for example,1,4,8,11-tetraazabicyclo[6.6.2]hexadecane-4,11-diyl)diacetic acid(CB-TE2A); desferoxamine (DFO); diethylenetriaminepentaacetic acid(DTPA); 1,4,7,10-tetraazacyclotetradecane-1,4,7,10-tetraacetic acid(DOTA); ethylene diamine tetraacetic acid (EDTA); ethyleneglycolbis(2-aminoethyl)-N,N,N′,N′-tetraacetic acid (EGTA);1,4,8,11-tetraazacyclotetradecane-1,4,8,11-tetraacetic acid (TETA);ethylenebis-(2-4 hydroxy-phenylglycine) (EHPG); 5-Cl-EHPG; 5Br-EHPG;5-Me-EHPG; 5t-Bu-EHPG; 5-sec-Bu-EHPG; benzodiethylenetriaminepentaacetic acid (benzo-DTPA); dibenzo-DTPA; phenyl-DTPA, diphenyl-DTPA;benzyl-DTPA; dibenzyl DTPA; bis-2(hydroxybenzyl)-ethylene-diaminediacetic acid (HBED) and derivativesthereof; Ac-DOTA; benzo-DOTA; dibenzo-DOTA; 1,4,7-triazacyclononaneN,N′,N″-triacetic acid (NOTA); benzo-NOTA; benzo-TETA, benzo-DOTMA,where DOTMA is 1,4,7,10-tetraazacyclotetradecane-1,4,7,10-tetra(methyltetraacetic acid), benzo-TETMA, where TETMA is1,4,8,11-tetraazacyclotetradecane-1,4,8,11-(methyl tetraacetic acid);derivatives of 1,3-propylenediaminetetraacetic acid (PDTA);triethylenetetraaminehexaacetic acid (TTHA); derivatives of1,5,10-N,N′,N″-tris(2,3-dihydroxybenzoyl)-tricatecholate (LICAM); and1,3,5-N,N′,N″-tris(2,3-dihydroxybenzoyl)aminomethylbenzene (MECAM), orother metal chelators, is used.

In certain embodiments the radioisotope-chelator pair is ⁸⁹Zr-DFO. Incertain embodiments, the radioisotope-chelator pair is ²²⁵Ac-DOTA. Incertain embodiments, the radioisotope-chelator pair is ¹⁷⁷Lu-DOTA. Incertain embodiments, the radioisotope-chelator pair is ⁶⁸Ga-DOTA. Incertain embodiments, other diagnostic, therapeutic, and/or theranosticradioisotope-chelators are used.

Described herein is a PET-based kinetic model which provides keyparameters regarding in vivo uptake in tumor and normal tissue. Asdescribed above, the saturability of the antibody-antigen interactionrequires non-linear analysis for patient-specific optimization oftherapeutic antibody-targeting strategies. As an example, describedherein is the development and initial validation of a non-linearcompartmental model of the in vivo biodistribution and kinetics of huA33based on PET imaging of ¹²⁴I-huA33. Other examples using variousantibodies are included to further validate the disclosed non-linearcompartmental model.

Routinely, radiolabeled mAbs are administered to patients at a singledefault dosage (e.g., such as 10 mg). However, the patient-specificoptimum dosage (e.g., the dosage which maximizes uptake in a targetedtumor and minimizes uptake in non-targeted normal organs) can deviatewidely among patients (perhaps over several orders of magnitude) fromthis default value, depending on such patient-dependent parameters asthe patient's body weight, the mass of the tumor, and the concentrationof the targeted antigen in the tumor, among many other parameters.

Serial non-invasive PET imaging of targeted antibody radiotracer uptakeand subsequent metabolism (e.g., efflux from antigen-positive site) wasutilized in conjunction with estimates of biological features of theradiolabeled target antigen interaction (e.g., targeting antibodyaffinity, antigen density, internalization rates of antibody-antigencomplex) to quantify internalization and turnover of an antibody-cargo(e.g., wherein the cargo comprises a radiolabel, cytotoxin, immuneeffectors, fluorescent probes, and/or porphyrins) using non-linearcompartmental modeling. This improves current radioimmunotherapy (RIT)and antibody-cytotoxic drug conjugates practices because thetumor-residence of the diagnostic radioisotope-antibody and subsequentkinetic modeling can be used to estimate the residence times ofalternative cargos associated with the same targeting antibody or toevaluate the potential impact of increasing the dose of targetingantibody on the cargo uptake and residence time.

Two clinically utilized long-lived positron-emitting isotopes, forexample, ⁸⁹Zr and ¹²⁴I have differences in biologic fate followingintracellular uptake, such as “residualizing” and “non-residualizing”,respectively. Pairwise evaluation of radiolabeled variants (such as“residualizing” and “non-residualizing”, respectively) of the sametargeting antibody in pre-clinical animal models can be useful toestimate differential response of tracer metabolism and quantitateturnover of bound antibody, thus guiding personalized treatment inpatients.

As an example, the choice of A33 antigen as the in vivo target was basedon features of the antigen-antibody interaction. Other antigens can beused. The expression of the A33 transmembrane glycoprotein is restrictedto normal human bowel and colon (95%), gastric (95%), and pancreatic(50%) cancers. Upon binding of anti-A33 mAb, the resultingnon-internalized antibody-antigen complex persists on the cell surfacefor several weeks. This membrane residence time is lengthy compared toother antibody-antigen complexes, and makes anti-A33 mAb particularlyattractive for application to RIT. In contrast, turnover ofA33-expressing cells in normal bowel is rapid, occurring every 5-6 daysby exfoliation of mucosal cells into the bowel lumen. This combinationof long persistence of antigen on the surface of tumor cells, asignificant “off-target” antigen pool in the colon, the high percentuptake in tumor, and demonstrated safety and tolerability of multipleadministrations in humans made the A33 antigen-antibody system asuitable model for initial studies with the PET-based non-linear kineticmodeling approach. Other antigen-antibody systems are also describedherein.

The compartmental-modeling approach described herein can be used topredict, on a patient-specific basis, the optimal therapeutic dose ofradiolabeled antibodies (e.g., huA33) as well as the antigen occupancy,residency times in normal and malignant tissues, and thecancer-to-normal tissue (e.g., colorectal) therapeutic index. In certainembodiments, a therapeutic index is defined as the PET image-derivedtumor-to-normal tissue (e.g., bowel mucosa, breast, prostate, lung,kidney, liver, salivary gland) cumulated activity concentration (%ID/g·h) ratio.

Moreover, this non-linear compartmental model can be readily applied tothe development of strategies such as multi-step targeting (MST)designed to further improve the therapeutic indices of RIT. Thisnon-linear compartmental model is not limited to radiolabeled huA33, butalso can be applied to various radiolabeled antibodies, including butnot limited, hu11B6 and J591, and other cargos (e.g., toxins, immuneeffectors, fluorescent probes, porphyrins, radiographic contrast agents(e.g., metals, MRI agents)) as described herein.

In the context of the a MST strategies, there are three general steps:administration of the non-radioactive antibody which specificallytargets and localizes in the tumor; a clearing agent, which is anon-radioactive reagent (e.g., streptavidin) which binds to any antibodystill in circulations and thereby accelerates its clearance from blood;and finally a small radioactive carrier molecule which is either rapidlycleared from the circulation (by virtue of its small size) or binds tothe antibody already localized in the tumor. In this way, delivery ofradioactivity and therefore the radiation dose to all normal tissues(e.g., all tissues other than tumor) are minimized by delivery ofradioactivity and radiation dose to tumor is maximized.

In certain embodiments, MST strategies, a non-radiolabeled mAb is firstadministered followed by a radiolabeled reagent which cross-reacts withthat mAb as well as a directly radiolabeled mAb.

EXPERIMENTAL EXAMPLES Example 1: Non-Linear Compartmental Modeling ofRadiolabeled Anti A33 mAb

For the current analysis, serial blood sampling, plasma counting, andwhole-body PET imaging were performed up to about 1 week followingintravenous injection of 10 mg of the humanized anti-A33 mAb (huA33)labeled with 10 mCi of ¹²⁴I into each of 11 colorectal cancer patients.The resulting kinetic data for each patient were fit to a compartmentalmodel using the SAAM II (Simulation, Analysis, and Modeling) program asis shown in FIG. 2. FIG. 3 shows that the model-predicted and measuredtime-activity data for one patient in the current cohort. For example,the non-linear compartmental analysis yielded a goodness of fit of themodel to the measured time-activity data; the model's goodness of fit tothis patient's kinetic data is typical of that for all 11 patientsanalyzed. For 10 of the 11 patients, the Z-statistic indicated that themodel fits were statistically acceptable at a 5% significance level. Thevalidity of this model is further corroborated by the agreement betweenthe antigen concentration in tumor derived from the model and from invitro binding studies of cell membrane fractions obtained from resectedtumor specimens as described by O'Donoghue et al., 2011. Among the threepatients in the cohort for whom the latter analysis was performed, themodel-derived and average binding study-derived antigen concentrationswere 30 nM and 45 nM, 45 nM and 54 nM, and 15 nM and 6.1 nM. The“best-fit” model parameters for the 11-patient cohort are summarized inTable 1.

In contrast to typical, “non-saturable” kinetics, the model presented inFIG. 2 is non-linear. As a result of the saturable binding of theanti-A33 mAb to the A33 antigenic sites that are generallycharacteristic of antibody-antigen interactions, the exchange rate forthe binding of the mAb to antigen progressively decreases as increasingnumbers of antigenic sites are occupied by the antibody. The practicalimplications of the non-linearity of antibody-antigen interactions forimmune targeting and the ability to predictively model theseinteractions on an individual-patient basis are important. For example,the concentration of A33 in targeted tumors, the total mass of theA33-expressing tumor, and the total number of A33 binding sites may varywidely among patients. Moreover, the anti-A33 mAb-to-A33 exchange ratein vivo, the tumor concentration, and tumor-to-normal tissue ratios ofthe antibody also varies. Variation also occurs because the dose ofantibody (in moles). As a result, there is no single optimum antibodydose which is translatable from patient to patient, even adjusted forbody mass, body surface area, etc. Rather, a more rational approach tooptimizing RIT requires administration of a pre-therapy tracer of themAb to the patient, an imaging-based measurement of its time-activitydata in blood, tumor, and antigen-expressing normal tissue(s), fittingof these data to the non-linear model, and model-based determination ofthe optimum antibody dose (in moles) for that patient. Thepatient-specific optimum antibody dose for MT is that which yields themaximum tumor-to-normal tissue area-under-the-curve (AUC) concentrationratios and thus the tumor-to-normal tissue therapeutic index. Forexample, as shown in FIG. 2, computer simulations using the “best-fit”model parameters for the patients whose images are shown in FIG. 1indicate that tumor targeting of huA33 mAb, expressed as thetumor-to-normal colon and tumor-to-plasma AUC concentration ratios, canbe nearly doubled by increasing the administered dose of antibody from10 to 50 mg. However, there would be little additional increase withfurther increase in the antibody dose (data not shown). Thus, once a“best-fit” model for the patient-specific tracer kinetics has beendeduced, the optimum mAb dose can be determined by model simulation oftumor, blood, and antigen-expressing normal-tissue kinetics (includingAUCs) for different, systematically varied mAb doses.

TABLE 1 “Best-Fit” Model Parameters and Model-derived Quantities forIntravenously Injected ¹²⁴I-anti-A33 Humanized mAb in Colorectal CancerPatients (n = 11) Standard Mean Deviation Range Normal-bowel A33concentration, 96 267 5-900 Total [A33]₂ (nM) Tumor A33 concentration,140 225 2-650 Total [A33]₃ (nM) (Anti-A33 mAb)-A33 association rate 6.1× 10¹⁰ 2.8 × 10¹⁰ 6.5 × 10⁹-1.1 × 10¹¹ constant, k_(a) (/M/h) Rate ofelimination of ¹²⁴I-mAb from 0.012 0.0023 0.0093-0.018   plasma, k(0,1)(/h) Rate of elimination of ¹²⁴I-mAb from 0.0056^(a) 0.007  0-0.02normal bowel, k(0,2) (/h) Rate of elimination of ¹²⁴I-mAb from0.0033^(a) 0.0078   0-0.020 tumor, k(0,3) (/h) Tumor-to-plasma AUCconcentration 6.1 8.8 0.16-26    (% ID/g-h) ratio^(b) Tumor-to-normalbowel mucosa AUC 4.5 5.1 0.13-16    concentration (% ID/g-h)ratio^(b, c) Maximum A33 occupancy (%)^(d) Tumor 53 41 1-100 Normalcolon 27 17 0.30-43    ^(a)Only 1 of the 11 patients had an eliminationrate from normal bowel of 0, while 8 of the 11 patients had anelimination rate from tumor of 0. ^(b)Corrected for radioactive decay of¹²⁴I to the time of injection. ^(c)The mucosa (assumed mass in allpatients: 7 g) is the A33-expressing component of the bowel wall.^(d)Maximum value of the percent of total A33 antigenic sites with¹²⁴I-anti-A33 humanized mAb bound.

In recent work demonstrating the utility of non-linear modeling incharacterizing immune reagents in vivo, the binding affinities andinternalization kinetics of ⁸⁹Zr-cG250 and ¹²⁴I-cG250, a chimericantibody directed against carbonic anhydrase IX (CAIX), werecharacterized in a human renal-cell carcinoma xenograft model in mice(SK-RC-38) by serial PET imaging and non-linear modeling. This work isdescribed in Cheal S M, Punzalan B, Doran M G, et al.: Pairwisecomparison of ⁸⁹Zr- and ¹²⁴I-labeled cG250 based on positron emissiontomography imaging and nonlinear immunokinetic modeling: in vivocarbonic anhydrase IX receptor binding and internalization in mousexenografts of clear-cell renal cell carcinoma. Eur J Nucl Med MolImaging. 41:985-94, 2014, which is hereby incorporated by reference inits entirety. ⁸⁹Zr- and ¹²⁴I-cG250 demonstrated virtually identicaltumor-cell binding and internalization rates but with markedly differenttumor retentions. Estimates of cG250-CAIX complex turnover were1.35-5.51×10¹² molecules per hour per gram of tumor (with 20% of thereceptors internalized per hour), and the ratio of ¹²⁴I-to-⁸⁹Zr atomsreleased per unit time by tumor was 18.

The properties of A33, and other antigens, as an antigenic target can beexploited and the foregoing paradigm to individualized RIT, includingMST-based RIT, of colorectal cancer and solid tumors generally, can beextended. As the data in Table 1 shows, directly labeled anti-A33 mAbtypically yields tumor-to-normal tissue ratios of the order of onlyseveral-to-one or less, which may be inadequate for safe and effectiveRIT. The physiology of A33-expressing normal cells makes A33 anexemplary target for MST-based RIT of colon cancer. By temporallydissociating delivery of the A33 mAb construct (e.g., streptavidinatedmAb or a bifunctional antibody construct) and the therapeuticradionuclide, potential therapy-limiting irradiation of the intestinalcrypt cells is avoided because the A33 mAb-binding crypt cells areturned over and replaced prior to administration of the radionuclidecarrier (e.g., metal chelator-biotin) designed to bind to the A33-mAbcomplex.

The MST strategy can utilize a bi-specific tetravalentanti-A33/-[metal-DOTA] antibody construct (nALDO) which reacts with A33and with radiometal-labeled DOTA-Bn (DOTA-Bn:((2-(4-aminobenzyl)-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetrayl)tetraacetic acid). Notably, these results demonstrate that acquisitionand accurate non-linear modeling of kinetic data of A33 andtumor-targeted immune constructs are clinically practical. Further, themodel utilized in the current analysis is adaptable to MST and thus canbe used to predict not only the patient-specific optimum dose (in mole)of the construct but also the optimum times of administration of theclearing agent and of the radionuclide carrier. The current results thusdemonstrate a practically achievable advancement in customized cancertherapy. Two clinically utilized long-lived positron-emitting isotopes⁸⁹Zr and ¹²⁴I have differences in biologic fate following intracellularuptake, such as “residualizing” and “non-residualizing”, respectively.Pairwise evaluation of radiolabeled variants (such as “residualizing”and “non-residualizing”, respectively) of the same targeting antibody inpre-clinical animal models can be useful to estimate differentialresponse of tracer metabolism and quantitate turnover of bound antibody,thus guiding personalized treatment in patients.

The present disclosure may be applicable to any antibody-antigen systemfor which antigen occupancy is the mechanism for anti-tumor effect ornormal-organ toxicity. Moreover, the present disclosure provides thebasis of a practical, scientifically rational, and cost-effectiveapproach to the clinical optimization of such therapeutics.

Methods and Materials A33 Antibody and Radiolabeling

HuA33 is a fully humanized IgG1 mAb derived from murine A33 antibody bycomplementarity determinant region (CDR) grafting and was produced andcharacterized at the Ludwig Institute for Cancer Research (Melbourne,Australia). HuA33 was labeled by the iodogen method with ¹²⁴I, producedin the Memorial Sloan-Kettering Cancer Center (MSKCC) cyclotron facilityor purchased from IBA (Dulle, Va.). Formulations of between 1 mCi¹²⁴I/10 mg A33 and 10 mCi ¹²⁴I/10 mg A33 were produced under currentGood Manufacturing Practices (cGMP) conditions. Briefly, ¹²⁴I wasincubated with huA33 (7 mg, 0.1 M phosphate buffer pH 7.4) in an iodogentube. After a 20-minute incubation, the mixture was purified through ananion exchange column followed by terminal sterilization through a0.22-μm filter. The activity of the final product was assayed in a dosecalibrator and cold antibody was added to yield a specific activity ofbetween 0.1 and 1 mCi/mg. All products passed pyrogen and sterilitytesting. The median radiochemical yield was 95%, radiochemical purity99.2%, and immunoreactivity 89% as determined by a cell-binding assayusing SW1222 cells. The use of a long-lived positron emitter, ¹²⁴I, andthe ability to thereby extend kinetic measurements to ˜1 weekpost-injection was an important practical feature of the current studyin terms of fitting of the disclosed model.

Patient Eligibility and Protocol Design

Patients were enrolled in an open-label prospective protocol approved bythe MSKCC Institutional Review Board (IRB) to evaluate the safety,pharmacokinetics, biodistribution, and tumor-targeting properties ofintravenously administered ¹²⁴I-huA33. Subjects were at least 18 yearsof age and had histologically confirmed primary or metastatic colorectaland were candidates for clinically indicated laparotomy for tumorresection or were non-surgical candidates with liver metastasis largerthan 2.5 cm. All subjects provided written and oral informed consent. Asubset of 11 (out of a total of 19) subjects having multiple PET orPET-CT scans were included in the current analysis.

Subjects received a single intravenous administration of ¹²⁴I-huA33 in5-30 ml of 5% human serum albumin over 5 to 20 minutes. Patientsscheduled for surgery underwent an ¹²⁴I-huA33 scan the morning of theirsurgery and surgically harvested tumor and normal colon were weighed andcounted in a sodium iodide scintillation well counter with a ¹²⁴Istandard to convert net count rates to activity and the activityconcentration (percent of the injected dose per gram, % ID/g)decay-corrected to the time of injection was determined.

Imaging and Derivation of Tissue Activities

Patients were imaged on a General Electric Advance PET scanner orDiscovery LS or DSTE PET-CT scanner in 2D mode with attenuation,scatter, and other standard corrections applied. Images were acquiredfor 6 minutes per bed position and included whole-torso imaging from theneck to the proximal thighs or from mid-chest to pelvis. For the cohortanalyzed in the current study, patients had their initial images 3 hoursof administration of ¹²⁴I-A33 and their final images at a median of 8.9days after tracer administration.

Images were read on a dedicated PET analysis workstation (AW Suite, GEHealthcare, Barrington, Ill.) by an experienced nuclear medicinephysician (JAC) who was aware of the patient's history and diagnostic CTreport. Localization in tumor was defined as focal accumulation greaterthan adjacent background in areas where physiologic activity isnegligible. Bowel activity foci were considered positive for tumor whenthere was an accumulation greater than that in adjacent bowel. Regionsof interest (ROIs) were manually placed over structures of interest andmean standardized uptake values (SUVs) ((nCi/mL activity in region)/(nCiinjected activity/body mass in g)) were determined for blood pool,tumor, and small and large bowel. In order to derive the mean tissueactivity concentration in % ID/g, the SUV was divided by the body weight(in g) and multiplied by 100.

Tumor masses were estimated by CT (assuming a mass density of 1 g/ml) orby weighing of surgically excised lesions. By multiplying the tumormasses determined by the PET-derived activity concentrations (in %ID/g), the total activities (in percent of the injected dose, % ID) intumor were determined at each of up to 4 imaging time points; anillustrative series of ¹²⁴I-huA33 PET images is shown in FIG. 1.A33-expressing cells in normal bowel are restricted to the intestinalmucosa, including the self-renewing intestinal crypt cells, and thuscomprise only a small portion by mass of the bowel wall: of the 1,000-gtotal mass of the bowel wall in the 70-kg “Reference Man”, only 7 gcomprise the intestinal mucosa according to the small-scale anatomicmodel for bowel. For all patients in the current analysis, therefore,the activities in the A33-expressing mucosa and the bowel wall werecalculated using 7 and 1,000 g as the respective masses of thesetissues.

Blood Sampling and Counting

Blood samples were drawn from all subjects from the contralateralantecubital vein as used for infusion at approximately 5, 15, 60, and120 minutes post-antibody administration and on the last day of imagingor the day of surgery (5-9 days after antibody infusion); for somesubjects, an additional blood sample was drawn at 24-72 hpost-administration with concomitant imaging. All samples werecentrifuged and plasma was aliquoted, weighed, and counted as describedabove for the tissue samples. The plasma activity concentrations in %ID/g were multiplied by the total plasma volume, calculated based on thepatient's height and weight, to yield the total plasma activity in % ID.

Compartmental Modeling

The plasma, normal bowel, and tumor time-activity data for each patientwere fit to the non-linear compartmental model, that is, the threedifferential equations, shown in FIG. 2 using the SAAM II program. Themeasured plasma time-activity data provided the direct input functionfor tumor and bowel, eliminating the need to explicitly account for theanti-A33 mAb in the rest of the body (e.g., in tissues other thanplasma, bowel, and tumor). For fitting, the starting values of theassociation rate constant (k_(a)), the total A33 concentration in normalbowel ([A33]₂), and the total A33 concentration in tumor ([A33]₃) were1×10¹¹/h/M, 2.5×10⁻⁸ M=24 nM, and 3.3×10⁻⁸ M=33 nM, respectively, asmeasured independently in vitro (data not shown). The other adjustablemodel parameters (with their starting values in parentheses) are: k(0,1)(0.01/h), k(0,2) (0.005/h), and k(0,3) (0/h); the value of k(0,2),0.005/h, corresponds to a half-life of slightly less than 6 d,consistent with the turnover rate of the intestinal mucosa. The model inFIG. 2 is based off the following assumptions: (i) no free radioiodineor radioiodinated metabolites of the ¹²⁴I-labeled anti-A33 mAb areproduced in vivo; (ii) there is no significant amount of free (e.g.,non-A33-bound mAb) in normal bowel and in tumor; (iii) there is nosignificant dissociation of the bound anti-A33 mAb from A33 (asindicated by the absence of k(2,0) and k(3,0) exchange rates in themodel and consistent with the finding of prolonged (up to 6-week)retention of radiolabeled A33 mAb in colorectal tumors); and (iv) the(anti-A33 mAb)-A33 association rate constant (k_(a)) is the same for A33in normal bowel and in tumor. Note that k(2,1) equals k_(a)·{Total[A33]₂-[(¹²⁴I-mAb) A33]₂} and that k(3,1) equals k_(a)·{Total[A33]₃-[(¹²⁴I-mAb)-A33]₃}; the second term (e.g., the differences) inthese two formulas make k(2,1) and k(3,1) time-varying parameters andthus introduces non-linearities into the model. Evaluation of the valuesof the various “k” parameters in the model for specific patients isperformed by computerized fitting of the model to the patient's measuredtime-activity data, that is, solving of the system of differentialequations in FIG. 2. The foregoing assumptions do not limit thegenerality of the proposed methodology in that the nonlinear compartmentmodel can be modified to account for those instances in which theseassumptions may not be satisfied.

FIG. 4 shows an illustrative network environment 400 for use in themethods and systems for analysis of spectrometry data corresponding toparticles of a sample, as described herein. In brief overview, referringnow to FIG. 4, a block diagram of an exemplary cloud computingenvironment 400 is shown and described. The cloud computing environment400 may include one or more resource providers 402 a, 402 b, 402 c(collectively, 402). Each resource provider 402 may include computingresources. In some implementations, computing resources may include anyhardware and/or software used to process data. For example, computingresources may include hardware and/or software capable of executingalgorithms, computer programs, and/or computer applications. In someimplementations, exemplary computing resources may include applicationservers and/or databases with storage and retrieval capabilities. Eachresource provider 402 may be connected to any other resource provider402 in the cloud computing environment 400. In some implementations, theresource providers 402 may be connected over a computer network 408.Each resource provider 402 may be connected to one or more computingdevice 404 a, 404 b, 404 c (collectively, 404), over the computernetwork 408.

The cloud computing environment 400 may include a resource manager 406.The resource manager 406 may be connected to the resource providers 402and the computing devices 404 over the computer network 408. In someimplementations, the resource manager 406 may facilitate the provisionof computing resources by one or more resource providers 402 to one ormore computing devices 404. The resource manager 406 may receive arequest for a computing resource from a particular computing device 404.The resource manager 406 may identify one or more resource providers 402capable of providing the computing resource requested by the computingdevice 404. The resource manager 406 may select a resource provider 402to provide the computing resource. The resource manager 406 mayfacilitate a connection between the resource provider 402 and aparticular computing device 404. In some implementations, the resourcemanager 406 may establish a connection between a particular resourceprovider 402 and a particular computing device 404. In someimplementations, the resource manager 406 may redirect a particularcomputing device 404 to a particular resource provider 402 with therequested computing resource.

FIG. 5 shows an example of a computing device 500 and a mobile computingdevice 550 that can be used in the methods and systems described in thisdisclosure. The computing device 500 is intended to represent variousforms of digital computers, such as laptops, desktops, workstations,personal digital assistants, servers, blade servers, mainframes, andother appropriate computers. The mobile computing device 550 is intendedto represent various forms of mobile devices, such as personal digitalassistants, cellular telephones, smart-phones, and other similarcomputing devices. The components shown here, their connections andrelationships, and their functions, are meant to be examples only, andare not meant to be limiting.

The computing device 500 includes a processor 502, a memory 504, astorage device 506, a high-speed interface 508 connecting to the memory504 and multiple high-speed expansion ports 510, and a low-speedinterface 512 connecting to a low-speed expansion port 514 and thestorage device 506. Each of the processor 502, the memory 504, thestorage device 506, the high-speed interface 508, the high-speedexpansion ports 510, and the low-speed interface 512, are interconnectedusing various busses, and may be mounted on a common motherboard or inother manners as appropriate. The processor 502 can process instructionsfor execution within the computing device 500, including instructionsstored in the memory 504 or on the storage device 506 to displaygraphical information for a GUI on an external input/output device, suchas a display 516 coupled to the high-speed interface 508. In otherimplementations, multiple processors and/or multiple buses may be used,as appropriate, along with multiple memories and types of memory. Also,multiple computing devices may be connected, with each device providingportions of the necessary operations (e.g., as a server bank, a group ofblade servers, or a multi-processor system).

The memory 504 stores information within the computing device 500. Insome implementations, the memory 504 is a volatile memory unit or units.In some implementations, the memory 504 is a non-volatile memory unit orunits. The memory 504 may also be another form of computer-readablemedium, such as a magnetic or optical disk.

The storage device 506 is capable of providing mass storage for thecomputing device 500. In some implementations, the storage device 506may be or contain a computer-readable medium, such as a floppy diskdevice, a hard disk device, an optical disk device, or a tape device, aflash memory or other similar solid state memory device, or an array ofdevices, including devices in a storage area network or otherconfigurations. Instructions can be stored in an information carrier.The instructions, when executed by one or more processing devices (forexample, processor 502), perform one or more methods, such as thosedescribed above. The instructions can also be stored by one or morestorage devices such as computer- or machine-readable mediums (forexample, the memory 504, the storage device 506, or memory on theprocessor 502).

The high-speed interface 508 manages bandwidth-intensive operations forthe computing device 500, while the low-speed interface 512 manageslower bandwidth-intensive operations. Such allocation of functions is anexample only. In some implementations, the high-speed interface 508 iscoupled to the memory 504, the display 516 (e.g., through a graphicsprocessor or accelerator), and to the high-speed expansion ports 510,which may accept various expansion cards (not shown). In theimplementation, the low-speed interface 512 is coupled to the storagedevice 506 and the low-speed expansion port 514. The low-speed expansionport 514, which may include various communication ports (e.g., USB,Bluetooth®, Ethernet, wireless Ethernet) may be coupled to one or moreinput/output devices, such as a keyboard, a pointing device, a scanner,or a networking device such as a switch or router, e.g., through anetwork adapter.

The computing device 500 may be implemented in a number of differentforms, as shown in the figure. For example, it may be implemented as astandard server 520, or multiple times in a group of such servers. Inaddition, it may be implemented in a personal computer such as a laptopcomputer 522. It may also be implemented as part of a rack server system524. Alternatively, components from the computing device 500 may becombined with other components in a mobile device (not shown), such as amobile computing device 550. Each of such devices may contain one ormore of the computing device 500 and the mobile computing device 550,and an entire system may be made up of multiple computing devicescommunicating with each other.

The mobile computing device 550 includes a processor 552, a memory 564,an input/output device such as a display 554, a communication interface566, and a transceiver 568, among other components. The mobile computingdevice 550 may also be provided with a storage device, such as amicro-drive or other device, to provide additional storage. Each of theprocessor 552, the memory 564, the display 554, the communicationinterface 566, and the transceiver 568, are interconnected using variousbuses, and several of the components may be mounted on a commonmotherboard or in other manners as appropriate.

The processor 552 can execute instructions within the mobile computingdevice 550, including instructions stored in the memory 564. Theprocessor 552 may be implemented as a chipset of chips that includeseparate and multiple analog and digital processors. The processor 552may provide, for example, for coordination of the other components ofthe mobile computing device 550, such as control of user interfaces,applications run by the mobile computing device 550, and wirelesscommunication by the mobile computing device 550.

The processor 552 may communicate with a user through a controlinterface 558 and a display interface 556 coupled to the display 554.The display 554 may be, for example, a TFT (Thin-Film-Transistor LiquidCrystal Display) display or an OLED (Organic Light Emitting Diode)display, or other appropriate display technology. The display interface556 may comprise appropriate circuitry for driving the display 554 topresent graphical and other information to a user. The control interface558 may receive commands from a user and convert them for submission tothe processor 552. In addition, an external interface 562 may providecommunication with the processor 552, so as to enable near areacommunication of the mobile computing device 550 with other devices. Theexternal interface 562 may provide, for example, for wired communicationin some implementations, or for wireless communication in otherimplementations, and multiple interfaces may also be used.

The memory 564 stores information within the mobile computing device550. The memory 564 can be implemented as one or more of acomputer-readable medium or media, a volatile memory unit or units, or anon-volatile memory unit or units. An expansion memory 574 may also beprovided and connected to the mobile computing device 550 through anexpansion interface 572, which may include, for example, a SIMM (SingleIn Line Memory Module) card interface. The expansion memory 574 mayprovide extra storage space for the mobile computing device 550, or mayalso store applications or other information for the mobile computingdevice 550. Specifically, the expansion memory 574 may includeinstructions to carry out or supplement the processes described above,and may include secure information also. Thus, for example, theexpansion memory 574 may be provided as a security module for the mobilecomputing device 550, and may be programmed with instructions thatpermit secure use of the mobile computing device 550. In addition,secure applications may be provided via the SIMM cards, along withadditional information, such as placing identifying information on theSIMM card in a non-hackable manner.

The memory may include, for example, flash memory and/or NVRAM memory(non-volatile random access memory), as discussed below. In someimplementations, instructions are stored in an information carrier and,when executed by one or more processing devices (for example, processor552), perform one or more methods, such as those described above. Theinstructions can also be stored by one or more storage devices, such asone or more computer- or machine-readable mediums (for example, thememory 564, the expansion memory 574, or memory on the processor 552).In some implementations, the instructions can be received in apropagated signal, for example, over the transceiver 568 or the externalinterface 562.

The mobile computing device 550 may communicate wirelessly through thecommunication interface 566, which may include digital signal processingcircuitry where necessary. The communication interface 566 may providefor communications under various modes or protocols, such as GSM voicecalls (Global System for Mobile communications), SMS (Short MessageService), EMS (Enhanced Messaging Service), or MMS messaging (MultimediaMessaging Service), CDMA (code division multiple access), TDMA (timedivision multiple access), PDC (Personal Digital Cellular), WCDMA(Wideband Code Division Multiple Access), CDMA2000, or GPRS (GeneralPacket Radio Service), among others. Such communication may occur, forexample, through the transceiver 568 using a radio-frequency. Inaddition, short-range communication may occur, such as using aBluetooth®, Wi-Fi™, or other such transceiver (not shown). In addition,a GPS (Global Positioning System) receiver module 570 may provideadditional navigation- and location-related wireless data to the mobilecomputing device 550, which may be used as appropriate by applicationsrunning on the mobile computing device 550.

The mobile computing device 550 may also communicate audibly using anaudio codec 560, which may receive spoken information from a user andconvert it to usable digital information. The audio codec 560 maylikewise generate audible sound for a user, such as through a speaker,e.g., in a handset of the mobile computing device 550. Such sound mayinclude sound from voice telephone calls, may include recorded sound(e.g., voice messages, music files, etc.) and may also include soundgenerated by applications operating on the mobile computing device 550.

The mobile computing device 550 may be implemented in a number ofdifferent forms, as shown in the figure. For example, it may beimplemented as a cellular telephone 580. It may also be implemented aspart of a smart-phone 582, personal digital assistant, or other similarmobile device.

Various implementations of the systems and techniques described here canbe realized in digital electronic circuitry, integrated circuitry,specially designed ASICs (application specific integrated circuits),computer hardware, firmware, software, and/or combinations thereof.These various implementations can include implementation in one or morecomputer programs that are executable and/or interpretable on aprogrammable system including at least one programmable processor, whichmay be special or general purpose, coupled to receive data andinstructions from, and to transmit data and instructions to, a storagesystem, at least one input device, and at least one output device.

These computer programs (also known as programs, software, softwareapplications or code) include machine instructions for a programmableprocessor, and can be implemented in a high-level procedural and/orobject-oriented programming language, and/or in assembly/machinelanguage. As used herein, the terms machine-readable medium andcomputer-readable medium refer to any computer program product,apparatus and/or device (e.g., magnetic discs, optical disks, memory,Programmable Logic Devices (PLDs)) used to provide machine instructionsand/or data to a programmable processor, including a machine-readablemedium that receives machine instructions as a machine-readable signal.The term machine-readable signal refers to any signal used to providemachine instructions and/or data to a programmable processor.

To provide for interaction with a user, the systems and techniquesdescribed here can be implemented on a computer having a display device(e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor)for displaying information to the user and a keyboard and a pointingdevice (e.g., a mouse or a trackball) by which the user can provideinput to the computer. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback (e.g., visual feedback,auditory feedback, or tactile feedback); and input from the user can bereceived in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in acomputing system that includes a back end component (e.g., as a dataserver), or that includes a middleware component (e.g., an applicationserver), or that includes a front end component (e.g., a client computerhaving a graphical user interface or a Web browser through which a usercan interact with an implementation of the systems and techniquesdescribed here), or any combination of such back end, middleware, orfront end components. The components of the system can be interconnectedby any form or medium of digital data communication (e.g., acommunication network). Examples of communication networks include alocal area network (LAN), a wide area network (WAN), and the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

Example 2: Comparative Biodistribution of Zirconium-89 (⁸⁹Zr)-DFO-hu11B6and Actinium-225 (²²⁵Ac)-DOTA-hu11B6 Antibody Targeting Breast Cancer inMice Bearing Human Breast Tumor Xenografts

Zirconium-89 (⁸⁹Zr) is a relatively-lived positron-emitting radioisotope(physical half-life: 3.27 days) which has been successfully used toradiolabel and image positron emission tomography (PET) human tumorxenografts in mice using tumor-targeted antibodies, several of which arenow in clinical trial, for example, ⁸⁹Zr-trastuzumab, ⁸⁹Zr-huJ591,⁸⁹Zr-MSTP2109A, ⁸⁹Zr-Df-IAB2M, ⁸⁹Zr-cmAb-U36, ⁸⁹Zr-ibritumomab tiuxetan,⁸⁹Zr-bevacizumab, and ⁸⁹Zr-rituximab. By serial PET imaging, the tumorand normal-tissue kinetics (e.g., time-activity data) of such⁸⁹Zr-labeled antibodies can be non-invasively measured in individualpatients. The non-linear compartmental model of antibody kineticsdescribed in herein is completely general and can be fit to the⁸⁹Zr-labeled antibody kinetics measured in specific patients.

As described herein, the fitted patient-specific model can be used forcomputer-based simulations of the antibody kinetics as a function of theantibody dose (in milligrams or millimoles, for example) and an optimumantibody dose determined. The optimum dose comprises the dose thatyields the highest tumor-to-normal tissue ratio of the antibodyconcentration integrated over time. The patient can then be administeredhis or her model-defined optimum antibody dose with the antibody nowcarrying a therapeutic payload such as anti-cancer drug or a therapeuticradionuclide.

In certain embodiments, anti-cancer drugs include any of the more than200 cancer drugs identified by the NIH athttp://www.cancer.gov/about-cancer/treatment/drugs, the contents ofwhich are hereby incorporated by reference in its entirety. Table 2shows exemplary drugs identified by the NIH.

TABLE 2 Exemplary drugs (Names A-J) Exemplary drugs (Names K-Z)Abiraterone Acetate Kadcyla (Ado-Trastuzumab Emtansine) Abitrexate(Methotrexate) Keoxifene (Raloxifene Hydrochloride) Abraxane (PaclitaxelAlbumin-stabilized Kepivance (Palifermin) Nanoparticle Formulation) ABVDKeytruda (Pembrolizumab) ABVE Kyprolis (Carfilzomib) ABVE-PC LanreotideAcetate AC Lapatinib Ditosylate AC-T Lenalidomide Adcetris (BrentuximabVedotin) Lenvatinib Mesylate ADE Lenvima (Lenvatinib Mesylate)Ado-Trastuzumab Emtansine Letrozole Adriamycin (DoxorubicinHydrochloride) Leucovorin Calcium Afatinib Dimaleate Leukeran(Chlorambucil) Afinitor (Everolimus) Leuprolide Acetate Akynzeo(Netupitant and Palonosetron Levulan (Aminolevulinic Acid)Hydrochloride) Aldara (Imiquimod) Linfolizin (Chlorambucil) AldesleukinLipoDox (Doxorubicin Hydrochloride Liposome) Alecensa (Alectinib)Lomustine Alectinib Lonsurf (Trifluridine and Tipiracil Hydrochloride)Alemtuzumab Lupron (Leuprolide Acetate) Alkeran for Injection (MelphalanLupron Depot (Leuprolide Acetate) Hydrochloride) Alkeran Tablets(Melphalan) Lupron Depot-Ped (Leuprolide Acetate) Alimta (PemetrexedDisodium) Lupron Depot-3 Month (Leuprolide Acetate) Aloxi (PalonosetronHydrochloride) Lupron Depot-4 Month (Leuprolide Acetate) Ambochlorin(Chlorambucil) Lynparza (Olaparib) Amboclorin (Chlorambucil) Marqibo(Vincristine Sulfate Liposome) Aminolevulinic Acid Matulane(Procarbazine Hydrochloride) Anastrozole Mechlorethamine HydrochlorideAprepitant Megestrol Acetate Aredia (Pamidronate Disodium) Mekinist(Trametinib) Arimidex (Anastrozole) Melphalan Aromasin (Exemestane)Melphalan Hydrochloride Arranon (Nelarabine) Mercaptopurine ArsenicTrioxide Mesna Arzerra (Ofatumumab) Mesnex (Mesna) Asparaginase Erwiniachrysanthemi Methazolastone (Temozolomide) Avastin (Bevacizumab)Methotrexate Axitinib Methotrexate LPF (Methotrexate) Azacitidine Mexate(Methotrexate) BEACOPP Mexate-AQ (Methotrexate) Becenum (Carmustine)Mitomycin C Beleodaq (Belinostat) Mitoxantrone Hydrochloride BelinostatMitozytrex (Mitomycin C) Bendamustine Hydrochloride MOPP BEP Mozobil(Plerixafor) Bevacizumab Mustargen (Mechlorethamine Hydrochloride)Bexarotene Mutamycin (Mitomycin C) Bexxar (Tositumomab and Iodine I 131Myleran (Busulfan) Tositumomab) Bicalutamide Mylosar (Azacitidine) BiCNU(Carmustine) Mylotarg (Gemtuzumab Ozogamicin) Bleomycin NanoparticlePaclitaxel (Paclitaxel Albumin- stabilized Nanoparticle Formulation)Blinatumomab Navelbine (Vinorelbine Tartrate) Blincyto (Blinatumomab)Necitumumab Bortezomib Nelarabine Bosulif (Bosutinib) Neosar(Cyclophosphamide) Bosutinib Netupitant and Palonosetron HydrochlorideBrentuximab Vedotin Neupogen (Filgrastim) Busulfan Nexavar (SorafenibTosylate) Busulfex (Busulfan) Nilotinib Cabazitaxel Ninlaro (IxazomibCitrate) Cabometyx (Cabozantinib-S-Malate) NivolumabCabozantinib-S-Malate Nolvadex (Tamoxifen Citrate) CAF Nplate(Romiplostim) Campath (Alemtuzumab) Obinutuzumab Camptosar (IrinotecanHydrochloride) Odomzo (Sonidegib) Capecitabine OEPA CAPOX OfatumumabCarac (Fluorouracil--Topical) OFF Carboplatin Olaparib CARBOPLATIN-TAXOLOmacetaxine Mepesuccinate Carfilzomib Oncaspar (Pegaspargase) Carmubris(Carmustine) Ondansetron Hydrochloride Carmustine Onivyde (IrinotecanHydrochloride Liposome) Carmustine Implant Ontak (Denileukin Diftitox)Casodex (Bicalutamide) Opdivo (Nivolumab) CeeNU (Lomustine) OPPA CEMOsimertinib Ceritinib Oxaliplatin Cerubidine (Daunorubicin PaclitaxelHydrochloride) Cervarix (Recombinant HPV Bivalent PaclitaxelAlbumin-stabilized Nanoparticle Vaccine) Formulation Cetuximab PADChlorambucil Palbociclib CHLORAMBUCIL-PREDNISONE Palifermin CHOPPalonosetron Hydrochloride Cisplatin Palonosetron Hydrochloride andNetupitant Clafen (Cyclophosphamide) Pamidronate Disodium ClofarabinePanitumumab Clofarex (Clofarabine) Panobinostat Clolar (Clofarabine)Paraplat (Carboplatin) CMF Paraplatin (Carboplatin) CobimetinibPazopanib Hydrochloride Cometriq (Cabozantinib-S-Malate) PCV COPDACPegaspargase COPP Peginterferon Alfa-2b COPP-ABV PEG-Intron(Peginterferon Alfa-2b) Cosmegen (Dactinomycin) Pembrolizumab Cotellic(Cobimetinib) Pemetrexed Disodium Crizotinib Perjeta (Pertuzumab) CVPPertuzumab Cyclophosphamide Platinol (Cisplatin) Cyfos (Ifosfamide)Platinol-AQ (Cisplatin) Cyramza (Ramucirumab) Plerixafor CytarabinePomalidomide Cytarabine Liposome Pomalyst (Pomalidomide) Cytosar-U(Cytarabine) Ponatinib Hydrochloride Cytoxan (Cyclophosphamide)Portrazza (Necitumumab) Dabrafenib Pralatrexate Dacarbazine PrednisoneDacogen (Decitabine) Procarbazine Hydrochloride Dactinomycin Proleukin(Aldesleukin) Daratumumab Prolia (Denosumab) Darzalex (Daratumumab)Promacta (Eltrombopag Olamine) Dasatinib Provenge (Sipuleucel-T)Daunorubicin Hydrochloride Purinethol (Mercaptopurine) DecitabinePurixan (Mercaptopurine) Defibrotide Sodium Radium 223 DichlorideDefitelio (Defibrotide Sodium) Raloxifene Hydrochloride DegarelixRamucirumab Denileukin Diftitox Rasburicase Denosumab R-CHOP DepoCyt(Cytarabine Liposome) R-CVP Dexamethasone Recombinant HumanPapillomavirus (HPV) Bivalent Vaccine Dexrazoxane HydrochlorideRecombinant Human Papillomavirus (HPV) Nonavalent Vaccine DinutuximabRecombinant Human Papillomavirus (HPV) Quadrivalent Vaccine DocetaxelRecombinant Interferon Alfa-2b Doxil (Doxorubicin HydrochlorideRegorafenib Liposome) Doxorubicin Hydrochloride R-EPOCH DoxorubicinHydrochloride Liposome Revlimid (Lenalidomide) Dox-SL (DoxorubicinHydrochloride Rheumatrex (Methotrexate) Liposome) DTIC-Dome(Dacarbazine) Rituxan (Rituximab) Efudex (Fluorouracil--Topical)Rituximab Elitek (Rasburicase) Rolapitant Hydrochloride Ellence(Epirubicin Hydrochloride) Romidepsin Elotuzumab Romiplostim Eloxatin(Oxaliplatin) Rubidomycin (Daunorubicin Hydrochloride) EltrombopagOlamine Ruxolitinib Phosphate Emend (Aprepitant) Sclerosol IntrapleuralAerosol (Talc) Empliciti (Elotuzumab) Siltuximab EnzalutamideSipuleucel-T Epirubicin Hydrochloride Somatuline Depot (LanreotideAcetate) EPOCH Sonidegib Erbitux (Cetuximab) Sorafenib Tosylate EribulinMesylate Sprycel (Dasatinib) Erivedge (Vismodegib) STANFORD V ErlotinibHydrochloride Sterile Talc Powder (Talc) Erwinaze (Asparaginase ErwiniaSteritalc (Talc) chrysanthemi) Etopophos (Etoposide Phosphate) Stivarga(Regorafenib) Etoposide Sunitinib Malate Etoposide Phosphate Sutent(Sunitinib Malate) Evacet (Doxorubicin Hydrochloride Sylatron(Peginterferon Alfa-2b) Liposome) Everolimus Sylvant (Siltuximab) Evista(Raloxifene Hydrochloride) Synovir (Thalidomide) Evomela (MelphalanHydrochloride) Synribo (Omacetaxine Mepesuccinate) Exemestane Tabloid(Thioguanine) 5-FU (Fluorouracil Injection) TAC 5-FU(Fluorouracil--Topical) Tafinlar (Dabrafenib) Fareston (Toremifene)Tagrisso (Osimertinib) Farydak (Panobinostat) Talc Faslodex(Fulvestrant) Talimogene Laherparepvec FEC Tamoxifen Citrate Femara(Letrozole) Tarabine PFS (Cytarabine) Filgrastim Tarceva (ErlotinibHydrochloride) Fludara (Fludarabine Phosphate) Targretin (Bexarotene)Fludarabine Phosphate Tasigna (Nilotinib) Fluoroplex(Fluorouracil--Topical) Taxol (Paclitaxel) Fluorouracil InjectionTaxotere (Docetaxel) Fluorouracil--Topical Temodar (Temozolomide)Flutamide Temozolomide Folex (Methotrexate) Temsirolimus Folex PFS(Methotrexate) Thalidomide FOLFIRI Thalomid (Thalidomide)FOLFIRI-BEVACIZUMAB Thioguanine FOLFIRI-CETUXIMAB Thiotepa FOLFIRINOXTolak (Fluorouracil--Topical) FOLFOX Topotecan Hydrochloride Folotyn(Pralatrexate) Toremifene FU-LV Torisel (Temsirolimus) FulvestrantTositumomab and Iodine I 131 Tositumomab Gardasil (Recombinant HPVTotect (Dexrazoxane Hydrochloride) Quadrivalent Vaccine) Gardasil 9(Recombinant HPV TPF Nonavalent Vaccine) Gazyva (Obinutuzumab)Trabectedin Gefitinib Trametinib Gemcitabine Hydrochloride TrastuzumabGEMCITABINE-CISPLATIN Treanda (Bendamustine Hydrochloride)GEMCITABINE-OXALIPLATIN Trifluridine and Tipiracil HydrochlorideGemtuzumab Ozogamicin Trisenox (Arsenic Trioxide) Gemzar (GemcitabineHydrochloride) Tykerb (Lapatinib Ditosylate) Gilotrif (AfatinibDimaleate) Unituxin (Dinutuximab) Gleevec (Imatinib Mesylate) UridineTriacetate Gliadel (Carmustine Implant) VAC Gliadel wafer (CarmustineImplant) Vandetanib Glucarpidase VAMP Goserelin Acetate Varubi(Rolapitant Hydrochloride) Halaven (Eribulin Mesylate) Vectibix(Panitumumab) Herceptin (Trastuzumab) VeIP HPV Bivalent Vaccine,Recombinant Velban (Vinblastine Sulfate) HPV Nonavalent Vaccine,Recombinant Velcade (Bortezomib) HPV Quadrivalent Vaccine, RecombinantVelsar (Vinblastine Sulfate) Hycamtin (Topotecan Hydrochloride)Vemurafenib Hydrea (Hydroxyurea) Venclexta (Venetoclax) HydroxyureaVenetoclax Hyper-CVAD Viadur (Leuprolide Acetate) Ibrance (Palbociclib)Vidaza (Azacitidine) Ibritumomab Tiuxetan Vinblastine Sulfate IbrutinibVincasar PFS (Vincristine Sulfate) ICE Vincristine Sulfate Iclusig(Ponatinib Hydrochloride) Vincristine Sulfate Liposome Idamycin(Idarubicin Hydrochloride) Vinorelbine Tartrate Idarubicin HydrochlorideVIP Idelalisib Vismodegib Ifex (Ifosfamide) Vistogard (UridineTriacetate) Ifosfamide Voraxaze (Glucarpidase) Ifosfamidum (Ifosfamide)Vorinostat IL-2 (Aldesleukin) Votrient (Pazopanib Hydrochloride)Imatinib Mesylate Wellcovorin (Leucovorin Calcium) Imbruvica (Ibrutinib)Xalkori (Crizotinib) Imiquimod Xeloda (Capecitabine) Imlygic (TalimogeneLaherparepvec) XELIRI Inlyta (Axitinib) XELOX Interferon Alfa-2b,Recombinant Xgeva (Denosumab) Interleukin-2 (Aldesleukin) Xofigo (Radium223 Dichloride) Intron A (Recombinant Interferon Alfa- Xtandi(Enzalutamide) 2b) Iodine I 131 Tositumomab and Yervoy (Ipilimumab)Tositumomab Ipilimumab Yondelis (Trabectedin) Iressa (Gefitinib) Zaltrap(Ziv-Aflibercept) Irinotecan Hydrochloride Zarxio (Filgrastim)Irinotecan Hydrochloride Liposome Zelboraf (Vemurafenib) Istodax(Romidepsin) Zevalin (Ibritumomab Tiuxetan) Ixabepilone Zinecard(Dexrazoxane Hydrochloride) Ixazomib Citrate Ziv-Aflibercept Ixempra(Ixabepilone) Zofran (Ondansetron Hydrochloride) Jakafi (RuxolitinibPhosphate) Zoladex (Goserelin Acetate) Jevtana (Cabazitaxel) ZoledronicAcid Zolinza (Vorinostat) Zometa (Zoledronic Acid) Zydelig (Idelalisib)Zykadia (Ceritinib) Zytiga (Abiraterone Acetate)

In certain embodiments, anti-cancer drugs used herein are classes ofdrugs including, but not limited to the following: calicheamicin,doxirubicin, dolstatin/auristatin, maytansine, emtansine, ravtansine,alpha amanitin, pyrolobenzodiazapine, tubulysins, dasatinib and otherpathway inhibitors, and bevatuzimab.

In certain embodiments, anti-cancer drugs used herein are classes ofdrugs targeting human epidermal growth factor receptor 3 (HER3), such asGSK2849330. HER3 expression is seen across a wide variety of solidmalignancies and is associated with poor prognosis. Up-regulation ofHER3 expression and activity is also associated with resistance tomultiple pathway inhibitors. In certain embodiments, an anti-cancer drugused herein includes GSK2849330, a mAb targeting HER3. In certainembodiments, the disclosed non-linear model characterizes thebiodistribution and dose-receptor occupancy relationship of GSK2849330in subjects with advanced HER3 expressing solid tumors via PET imaging.Such a characterization is conducted in two parts. Part 1 includes theimaging phase where each subject will receive two doses of GSK2849330containing both ⁸⁹Zr labelled GSK2849330 and unlabeled GSK2849330. Theamount of unlabeled GSK2849330 present in each dose is varied to explorethe effect on target mediated uptake of ⁸⁹Zr into HER3 expressingtissues and tumors. Subjects then proceed to the continuation phase (orPart 2) for continued treatment with unlabeled GSK2849330.

In certain embodiments, anti-cancer drugs used herein are classes ofdrugs targeting human carcinoembryonic antigen (e.g., CEA, CD66e) oncells (e.g., tumor cells, cluster of differentiation 3 (CD3) positiveT-cells), such as AMG 211. AMG 211 is a bispecific single-chain antibodyconstruct of the bispecific T-cell engager class and is a targeted drugin the treatment of relapsed/refractory gastrointestinal adenocarcinoma,or other CEA expressing tumors.

A well-known challenge in current drug development using targetedtherapies is the high level of heterogeneity of target expression thatis present in specific tumor types. The disclosed non-linearcompartmental model, including radiolabeling of antibodies, provides amethodology to overcome this challenge.

In FIG. 6, the biodistributions (or the percent of the administeredactivity per gram of tissue, % IA/g) at 244 hours post-administration of⁸⁹Zr-DFO-hu11B6 and ²²⁵Ac-DOTA-hu11B6 antibody targeting breast cancerin mice bearing human BT474 breast tumor xenografts are compared andshown, within experimental error, to be identical (lg-int: largeintestine; sm-int: small intestine). Notably, even with theadministration of hormones, progesterone (“Prog.”) or estrogen(“Estro.”), which impact the levels of expression of the hu11B6 targetantigen, the biodistributions of these two radiolabeled forms of thehu11B6 antibody (Diaprost, Inc.; Lund, Sweden) remained identical. Thus,(1) the biodistribution, including tumor uptake, of the therapeutic²²⁵Ac-DOTA-hu11B6 antibody can be accurately measured prior to therapywith the diagnostic ⁸⁹Zr-DFO-hu11B6 antibody and (2) the model-predictedoptimum dose of hu11B6 antibody, based on the measured kinetics of⁸⁹Zr-DFO-hu11B6 antibody, is the same for both ⁸⁹Zr-DFO-hu11B6 and²²⁵Ac-DOTA-hu11B6 antibody. While the biodistribution data shown in FIG.6 are for single time point, 244 hours, post administration ofradiolabeled hu11B6, as with all systemically administered materials,can change the biodistribution profiles with time and thus requirekinetic (e.g., time-varying) analysis (such as that provided by thenon-linear compartmental modeling paradigm) to reliably derive anoptimum antibody dose for a given patient.

²²⁵Ac is an alpha particle-emitting radioisotope. Although alphaparticles-emitters are not yet in widespread clinical use, they haveunique and highly advantageous physical and biological properties fortargeted therapy: (1) their ranges in tissues are very short (of theorder of one hundredth of a millimeter or approximately one cell radius)and thus will deliver their radiation doses very locally and veryselectively to the targeted tumor cells with little or no significantirradiation of nearby normal cells; and (2) their very high so-calledlinear energy transfer (or LET) and resulting ionization density meansthat as few a single alpha-particle traversing a tumor-cell nucleus cankill the cell. Alpha-particles emitting radionuclides such as ²²⁵Ac aretherefore highly attractive therapeutic payloads for application to thestrategy described herein. And, to reiterate, the identicalbiodistributions of ⁸⁹Zr-DFO-hu11B6 and ²²⁵Ac-DOTA-hu11B6 antibodystrongly support the clinical feasibility of this novel strategy.

Example 3: Targeting of Radiolabeled J591 Antibody to PSMA-expressingtumors: Optimization of Imaging and Therapy Based on Non-LinearCompartmental Modeling

Further confirming the workability of the non-linear compartmentalmodel, FIG. 7 shows the disclosed non-linear compartmental model appliedto a quantitative comparison of absolute uptake and antibody turnover inprostate cancer (PCa) of the anti-prostate-specific membrane antigen(PSMA) antibody J591 labeled with ⁸⁹Zr (or ⁸⁹Zr-J591). The model wasapplied to serial PET data measured following intravenous injection intomice each bearing a subcutaneous human PCa xenograft tumor. In additionto modeling “low” radiolabeled antibody doses (e.g., less than 100micrograms of Ab per mouse), tumor-bearing mice were also administeredincreasing doses of radiolabeled J591 antibody up to 2,000 microgramsper mouse, and the time-activity data in the respective groups of miceagain measured by PET. Among the parameters derived from the resultingkinetic data was the so-called “targeting index” (e.g., thetumor-to-plasma ratio of the integrals of the tumor and plasma activityconcentrations); the targeting index is a metric of the overalltherapeutic effectiveness of radiolabeled J591 therapy because itreflects the relative radiation doses delivered to the target tissue(e.g., the tumor) and the therapy-limiting normal tissue (e.g., theplasma, or blood, as a surrogate of the hematopoietic bone marrow). Asshown in FIG. 7, the non-linear antibody compartmental model, incontrast to the corresponding linear model, accurately predicted thetargeting index as function of antibody dose, confirming that onlynon-linear modeling (as described herein) can reliably be used forpatients-specific dose optimization. Note that the data in FIG. 7 havebeen published as follows: Fung E K, Cheal S M, Fareedy S B, Punzalan B,Beylergil V, Amir J, Chalasani S, Weber W A, Spratt D E, Veach D R,Bander N H, Larson S M, Zanzonico P B, Osborne J R. Targeting ofradiolabeled J591 antibody to PSMA-expressing tumors: optimization ofimaging and therapy based on non-linear compartmental modeling. Eur JNucl Med Mol Imaging Res 6, 2016, which is incorporated by referenceherein in its entirety.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned herein arehereby incorporated by reference in their entirety as if each individualpublication, patent or patent application was specifically andindividually indicated to be incorporated by reference. In case ofconflict, the present application, including any definitions herein,will control.

EQUIVALENTS

While the invention has been particularly shown and described withreference to specific preferred embodiments, it should be understood bythose skilled in the art that various changes in form and detail may bemade therein without departing from the spirit and scope of theinvention as defined by the appended claims.

1. A method for determining a patient-specific optimum mAb dose, themethod comprising: administering to a subject a radiolabeled monoclonalantibody (mAb) as pre-therapy tracer of the mAb; imaging the subject toobtain a plurality of sets of images acquired over a period of time,wherein each set of images is acquired at a particular time followingadministration of the radiolabeled monoclonal antibody, and wherein eachset of images visualizes a blood pool, tumor, and antigen-positivenormal tissue; determining, by a processor of a computing device, fromthe plurality of sets of images, measurements of time-activity of thepre-therapy tracer of the mAb in blood, tumor, and antigen-expressingnormal tissue for the subject; identifying, by the processor, one ormore parameters of a nonlinear compartmental model of thekinetics/biodistribution of the pre-therapy tracer in the subject fromthe determined measurements of time-activity for the subject; anddetermining, by the processor, a patient-specific optimal therapeuticantibody dose (or multiple doses) for the subject using the nonlinearcompartmental model with the identified one or more parameters.
 2. Themethod of claim 1, wherein the monoclonal antibody is selected from thegroup consisting of A33 antibody, hu11B6 antibody, J591 antibody,trastuzumab, MSTP2109A, Df-IAB2M, cmAb-U36, ibritumomab tiuxetan,bevacizumab, rituximab, any antibody targeting human epidermal growthfactor receptor 3, and any antibody targeting human carcinoembryonicantigen.
 3. The method of claim 1, wherein the radiolabel is selectedfrom the group consisting of ^(99m)Tc, ⁶⁴Cu, ⁶⁷Ga, ¹⁸⁶Re, ¹⁵³Sm, ¹⁷⁷Lu,⁶⁷Cu, ¹²³I, ¹²⁴I, ¹²⁵I, ¹¹C, ¹³N, ¹⁵O, ¹⁸F, ¹⁸⁶Re, ¹⁸⁸Re, ¹⁵³Sm, ¹⁶⁶Ho,¹⁷⁷Lu, ¹⁴⁹Pm, ⁹⁰Y, ²¹³Bi, ¹⁰³Pd, ¹⁰⁹Pd, ¹⁵⁹Gd, ¹⁴⁰La, ¹⁹⁸Au, ¹⁹⁹Au,¹⁶⁹Yb, ¹⁷⁵Yb, ¹⁶⁵Dy, ¹⁶⁶Dy, ⁶⁷Cu, ¹⁰⁵Rh, ¹¹¹Ag, ⁸⁹Zr, ²²⁵Ac, ¹³¹I, ²¹²B,and ¹⁹²Ir.
 4. The method of claim 3, wherein the radiolabel comprises⁸⁹Zr.
 5. The method of claim 3, wherein the radiolabel comprises ²²⁵Ac.6. The method of claim 3, wherein the radiolabel comprises ¹²⁴I.
 7. Themethod of claim 1, further comprising acquiring the plurality of sets ofimages via positron emission tomography (PET).
 8. The method of claim 7,wherein a first set of the images is acquired several hours afterantibody administration and at least a second set of the images isacquired up to 2 weeks after antibody administration.
 9. The method ofclaim 8, wherein the first set of images is acquired at least 2 hours,at least 3 hours, at least 4 hours, or at least 5 hours after antibodyadministration.
 10. The method of claim 1, wherein the antigen-positivenormal tissue comprises colon tissue.
 11. The method of claim 1, whereinthe antigen-positive normal tissue comprises breast tissue.
 12. Themethod of claim 1, wherein the antigen-positive normal tissues compriseprostate tissue.
 13. The method of claim 1, wherein the therapeuticantibody is the same monoclonal antibody as in the pre-therapy tracer.14. The method of claim 1, wherein the therapeutic antibody is differentthan the monoclonal antibody in the pre-therapy tracer.
 15. The methodof claim 1, wherein the measurements of time-activity of the pre-therapytracer comprise one or more members selected from the group consistingof a mean activity concentration of the radiolabeled antibody in theblood, a mean activity concentration of the radiolabeled antibody in thetumor, and a mean activity concentration of the radiolabeled antibody inthe antigen-expressing normal tissue.
 16. The method of claim 15,wherein the antigen-expressing normal tissue comprises a member selectedfrom the group consisting of bowel tissue, breast tissue, prostatetissue, kidney, liver, salivary gland, and lung.
 17. The method of claim1, further comprising imaging the subject via computed tomography (CT)or magnetic resonance imaging (MRI) to determine a tumor mass.
 18. Themethod of claim 17, wherein a mean activity concentration of theradiolabeled antibody in the tumor is determined, by the processor, froma total activity concentration by multiplying the tumor mass by thetotal activity concentration and an elapsed time after the administeringstep.
 19. The method of claim 1, wherein the radiolabeled mAb comprisesa radiolabel with a half-life compatible with imaging-based activityconcentration measurements in tumor and other tissues.
 20. The method ofclaim 19, wherein the half-life is consistent with the turnover rate ofintestinal mucosa in the case of A33 mAb.
 21. The method of claim 1,wherein the radiolabeled mAb is administered over a period of time from5 minutes to 20 minutes.
 22. The method of claim 1, wherein theadministering step comprises administering two or more doses of thepre-therapy tracer at different times, and wherein the method furthercomprises determining, by the processor, one or more optimum times ofadministration of the pre-therapy tracer (non-radiolabeled mAb).
 23. Themethod of claim 22, wherein the pre-therapy tracer comprises abi-specific tetravalent antibody/-[metal-DOTA] antibody construct(nALDO, or huA33-C825) which reacts with an antigen and withradiometal-labeled DOTA-Bn (DOTA-Bn:(2-(4-aminobenzyl)-1,4,7,10-tetraazacyclododecane-1,4,7,10-tetrayl)tetraacetic acid).
 24. The method of claim 23, wherein the antibody is amember selected from the group consisting of A33 antibody, hu11B6antibody, and J591 antibody and the antigen is a member selected fromthe group consisting of A33, hu11B6, and J591.
 25. The method of claim1, wherein the nonlinear compartmental model comprises one or more ofthe following assumptions: (i) no free radioiodine or radioiodinatedmetabolites of the radiolabeled mAb are produced in vivo; (ii) there isno significant amount of free (non-bound) radiolabeled mAb in normaltissue and in tumor tissue; (iii) there is no significant dissociationof the bound radiolabeled mAb from the antigen; and (iv) the mAb-antigenassociation rate constant (ka) is the same for antigen in normal tissueand in tumor.
 26. The method of claim 25, wherein the radiolabeled mAbis a member selected from the group consisting of ¹²⁴I-labeled anti-A33,⁸⁹Zr-labeled anti-hu11B6, ⁸⁹Zr-labeled anti-J591, ⁸⁹Zr-trastuzumab,⁸⁹Zr-MSTP2109A, ⁸⁹Zr-Df-IAB2M, ⁸⁹Zr-cmAb-U36, ⁸⁹Zr-ibritumomab tiuxetan,⁸⁹Zr-bevacizumab, and ⁸⁹Zr-rituximab.
 27. The method of claim 25,wherein the antigen is a member selected from the group consisting ofA33, hu11B6, J591, GPA33, kallikrein hk2, and prostate specific membrane(PSMA).
 28. The method of claim 25, wherein the tissue is a memberselected from the group consisting of bowel tissue, breast tissue,prostate tissue, kidney, liver, salivary gland, and lung.
 29. The methodof claim 1, comprising determining, by the processor, a quantificationof internalization and turnover of an antibody-cargo via the nonlinearcompartmental model.
 30. The method of claim 29, wherein theantibody-cargo comprises a member selected from the group consisting ofa radiolabel, anti-cancer drug, immune effectors, fluorescent probes,porphyrins, radiographic contrast agents, and cytotoxin.
 31. The methodof claim 30, wherein the anti-cancer drug comprises a member selectedfrom the group consisting of calicheamicin, doxirubicin,dolstatin/auristatin, maytansine, emtansine, ravtansine, alpha amanitin,pyrolobenzodiazapine, tubulysins, rituximab, trastuzumab, bevacizumab,anti-huJ591, MSTP2109A, Df-IAB2M, cmAb-U36, ibritumomab tiuxetan,bevacizumab, rituximab, any antibody targeting human epidermal growthfactor receptor 3, and any antibody targeting human carcinoembryonicantigen, dasatinib and other pathway inhibitors, bevatuzimab, cetuximab,and at least one drug depicted in Table
 2. 32. The method of claim 30,wherein the radiolabel comprises a member selected from the groupconsisting of ²²⁵Ac, ¹⁷⁷Lu, ²¹¹At, ¹³¹I, ¹²⁴I, ⁹⁰Y, ⁶⁴Cu, and ⁶⁷Cu. 33.The method of claim 29, wherein the nonlinear compartmental modelcomprises a representation of an interaction of the pre-therapy tracerof the mAb with tissue.
 34. The method of claim 29, wherein theinteraction comprises a member selected from the group consisting oftargeting antibody affinity, antigen density, and internalization ratesof antibody-antigen complex.
 35. The method of claim 1, wherein thenonlinear compartmental model is as depicted in FIG.
 2. 36. The methodof claim 29, wherein the administering step comprises administering amonoclonal antibody with a first cargo and a second cargo, the methodfurther comprising estimating, by the processor, a residence time of thefirst and second antibody cargos associated with the same (type of) mAb.37. The method of claim 1, further comprising administering thetherapeutic antibody dose (or multiple doses) to the subject.
 38. Themethod of claim 1, wherein the administration is via injection.
 39. Themethod of claim 1, wherein the one or more parameters comprises atargeting index.
 40. The method of claim 39, wherein the targeting indexcomprises a tumor-to-plasma ratio of integrals of tumor and plasmaactivity concentrations.
 41. The method of claim 1, wherein the one ormore parameters comprises a therapeutic index.
 42. The method of claim41, wherein the therapeutic index comprises a PET image-derivedtumor-to-normal tissue ratio.
 43. The method of claim 42, wherein thetumor-to-normal tissue comprises a member selected from the groupconsisting of tumor-to-bowel mucosa, tumor-to-breast, tumor-to-prostate,tumor-to-lung, tumor-to-kidney, tumor-to-liver, tumor-to-salivary gland,tumor-to-bone marrow, tumor-to-liver, tumor-to-lung, tumor-to-kidney,tumor-to-bowel, tumor-to-bladder, tumor-to-gonads, and tumor-to-brain.44. A method of determining a patient-specific optimum mAb dose, themethod comprising: receiving, by a processor of a computing device, aplurality of sets of images acquired over a period of time, wherein eachset of images is acquired at a particular time following administrationof the radiolabeled monoclonal antibody, and wherein each set of imagesvisualizes a blood pool, tumor, and antigen-positive normal tissue;determining, by the processor, from the plurality of sets of images,measurements of time-activity of the pre-therapy tracer of the mAb inblood, tumor, and antigen-expressing normal tissue for the subject;identifying, by the processor, one or more parameters of a nonlinearcompartmental model of the kinetics/biodistribution of the pre-therapytracer in the subject from the determined measurements of time-activityfor the subject; and determining, by the processor, an optimumtherapeutic antibody dose (or multiple doses) for the subject using thenonlinear compartmental model with the identified one or moreparameters.
 45. The method of claim 44, further comprising acquiring theplurality of sets of images via positron emission tomography (PET). 46.The method of claim 44, wherein a first set of the images is acquiredseveral hours after post-antibody administration and at least a secondset of the images is acquired up to 2 weeks after antibodyadministration.
 47. The method of claim 46, wherein the first set ofimages is acquired at least 2 hours, at least 3 hours, at least 4 hours,or at least 5 hours after post-antibody administration.
 48. The methodof claim 44, wherein the antigen-positive normal tissue comprises colontissue.
 49. The method of claim 44, wherein the antigen-positive normaltissue comprises breast tissue.
 50. The method of claim 44, wherein theantigen-positive normal tissue comprises prostate tissue.
 51. The methodof claim 44, wherein the therapeutic antibody is the same monoclonalantibody as in the pre-therapy tracer.
 52. The method of claim 44,wherein the therapeutic antibody is different than the monoclonalantibody in the pre-therapy tracer.
 53. The method of claim 44, whereinthe one or more parameters comprise a targeting index.
 54. The method ofclaim 53, wherein the targeting index comprises a tumor-to-plasma ratioof integrals of tumor and plasma activity concentrations.
 55. The methodof claim 44, wherein the one or more parameters comprises a therapeuticindex.